text:
\section{Introduction}
Dense object detector~\cite{redmon2016you,liu2016ssd,zhou2019objects,tian2019fcos,li2020generalized,qiu2020borderdet} which directly predicts pixel-level object categories and bounding boxes
over feature maps,
becomes increasingly popular due to its elegant and effective
framework.
One of the crucial techniques underlying this framework is Localization Quality Estimation (LQE).
With the help of better LQE, high-quality bounding boxes tend to score higher than low-quality ones, greatly reducing the risk of mistaken suppression in Non-Maximum Suppression (NMS) processing.

\iffalse
\begin{figure*}[t]
	\begin{center}
		\setlength{\fboxrule}{0pt}

		\fbox{\includegraphics[width=\textwidth]{./figs/distribution_quality_cropped.pdf}}
	\end{center}
	\captionsetup{font={small}}
	\vspace{-20pt}
	\caption{Motivation of utilizing the highly relevant statistics of learned bounding box distributions to guide the better generation of its estimated localization quality. The sharp distribution corresponds to higher quality, whilst the flat one stands for lower quality usually. Green: predicted bounding boxes; White: ground-truth bounding boxes.}
	\label{fig_distribution_quality_cropped}
	\vspace{-10pt}
\end{figure*}
\fi

Many previous works~\cite{redmon2016you,redmon2017yolo9000,redmon2018yolov3,tian2019fcos,zhang2019bridging,wu2020iou,kim2020probabilistic,li2020generalized,zhang2020varifocalnet,zhu2020autoassign,qiu2020borderdet} have explored LQE.
For example,
the YOLO family \cite{redmon2016you,redmon2017yolo9000,redmon2018yolov3} first adopt \emph{Objectness} to describe the localization quality, which is defined as the Intersection-over-Union (IoU) between the predicted and ground-truth box.
After that, \emph{IoU} is further explored and proved to be effective in IoU-Net \cite{jiang2018acquisition}, IoU-aware \cite{wu2020iou}, PAA \cite{kim2020probabilistic}, GFLV1 \cite{li2020generalized} and VFNet \cite{zhang2020varifocalnet}.
Recently, FCOS~\cite{tian2019fcos} and ATSS~\cite{zhang2019bridging} introduce
\emph{Centerness}, the distance degree to the object center, to suppress low-quality detection results.
Generally, the aforementioned methods share a common characteristic that they are all based on \emph{vanilla convolutional
features}, e.g., features of points, borders or regions (see Fig.~\ref{fig_comparison_concept} (a)-(g)), to estimate the localization quality.

Different from previous works,
in this paper,
we explore
a brand new
perspective to conduct LQE -- by
directly utilizing \emph{the statistics of bounding box distributions}, instead of using the \emph{vanilla convolutional features} (see Fig.~\ref{fig_comparison_concept}).
Here the \emph{bounding box distribution} is introduced as ``General Distribution'' in GFLV1~\cite{li2020generalized}, where it learns a discrete probability distribution of each predicted edge (Fig.~\ref{fig_concept} (a)) for describing the uncertainty of bounding box regression.
Interestingly,
we
observe that
the statistic
of the General Distribution has a strong correlation with its real localization quality, as illustrated in Fig.~\ref{fig_concept} (b). More specifically in Fig.~\ref{fig_concept} (c) and (d), the shape (flatness) of bounding box distribution can clearly reflect the
localization quality of the predicted results:
the sharper the distribution, the more accurate the predicted bounding box, and vice versa. Consequently, it can potentially be easier and very efficient to conduct better LQE by the guidance of the distribution information, as the input (distribution statistics of bounding boxes) and the output (LQE scores) are highly correlated.

Inspired by the strong correlation between the distribution statistics and LQE scores, we propose a very lightweight sub-network with only dozens of (e.g., 64) hidden units, on top of
these distribution statistics to produce reliable LQE scores, significantly boosting the detection performance. Importantly, it brings negligible additional computation cost in practice and almost does not affect the training/inference speed of the basic object detectors. In this paper, we term this lightweight sub-network as Distribution-Guided Quality Predictor (DGQP), since it relies on the guidance of distribution statistics for quality predictions.

By introducing the lightweight DGQP that predicts reliable LQE scores via statistics of bounding box distributions, we develop a novel dense object detector based on the framework of GFLV1, thus termed GFLV2.
To verify the effectiveness of GFLV2, we conduct extensive experiments on the challenging benchmark COCO~\cite{lin2014microsoft}.
Notably, based on ResNet-101 \cite{he2016deep}, GFLV2 achieves impressive detection performance (46.2 AP), i.e., 2.6 AP gains over the state-of-the-art ATSS baseline (43.6 AP) on COCO {\tt test-dev}, under the same training schedule and without sacrificing the efficiency both in training and inference.

In summary, our contributions are as follows:

$\bullet$ To our best knowledge, our work is the first to bridge the statistics of
bounding box distributions and
localization quality estimation in an end-to-end dense object detection
framework.

$\bullet$ The proposed GFLV2 is considerably lightweight and cost-free in practice. It can also be easily plugged into most dense object detectors with a consistent gain of $\sim$2 AP, and without loss of training/inference speed.

$\bullet$
Our GFLV2 (Res2Net-101-DCN)
achieves very competitive 53.3 AP (multi-scale testing) on COCO dataset among dense object detectors.

\section{Related Work}
\noindent\textbf{Formats of LQE}:
Early popular object detectors \cite{girshick2015fast,ren2015faster,cai2018cascade,he2017mask}
simply treat the classification confidence as the formulation of LQE score,
but there is an obvious inconsistency between them, which inevitably degrades the detection performance.
To alleviate this problem, AutoAssign \cite{zhu2020autoassign} and BorderDet \cite{qiu2020borderdet} employ additional
localization features to rescore the classification confidence, but they still lack an explicit definition of LQE.

Recently, FCOS \cite{tian2019fcos} and ATSS \cite{zhang2019bridging} introduce a novel format of LQE, termed \emph{Centerness}, which depicts the distance degree to the center of the object.
Although \emph{Centerness} is
effective,
recent researches \cite{li2020generalized,zhang2020varifocalnet} show that it has certain limitations and may be suboptimal for LQE.
SABL \cite{wang2019side} introduces boundary buckets for coarse localization, and utilizes the averaged bucketing confidence as a formulation of LQE.

After years of
technical iterations~\cite{redmon2016you,redmon2017yolo9000,redmon2018yolov3,jiang2018acquisition,tychsen2018improving,huang2019mask,wu2020iou,kim2020probabilistic,li2020generalized,zhang2020varifocalnet}, \emph{IoU} has been deeply studied and becomes increasingly popular as an excellent measurement of LQE.
\emph{IoU} is first known as the \emph{Objectness} in YOLO \cite{redmon2016you,redmon2017yolo9000,redmon2018yolov3}, where the network is supervised to produce estimated IoUs between predicted boxes and ground-truth ones, to
reduce ranking basis during NMS.
Following the similar paradigm, IoU-Net \cite{jiang2018acquisition}, Fitness NMS \cite{tychsen2018improving}, MS R-CNN \cite{huang2019mask}, IoU-aware \cite{wu2020iou}, PAA \cite{kim2020probabilistic} utilize a separate branch to perform LQE in the \emph{IoU} form.
Concurrently, GFLV1 \cite{li2020generalized} and VFNet \cite{zhang2020varifocalnet} demonstrate a more effective format, by merging the classification score with \emph{IoU} to reformulate a joint representation.
Due to its great success \cite{li2020generalized,zhang2020varifocalnet}, we build our GFLV2 based on
the Classification-IoU Joint Representation \cite{li2020generalized},
and develop a novel approach for reliable LQE.

\noindent\textbf{Input Features for LQE}: As shown in the left part of Fig.~\ref{fig_comparison_concept}, previous works directly use
convolutional features as input for
LQE, which only differ in the way of spatial sampling.
Most existing methods~\cite{redmon2016you,redmon2017yolo9000,redmon2018yolov3,tian2019fcos,zhang2019bridging,wu2020iou,kim2020probabilistic,li2020generalized} adopt the point features (see Fig.~\ref{fig_comparison_concept} (a)) to produce LQE scores for high efficiency.
IoU-Net \cite{jiang2018acquisition} predicts IoU based on the region features as shown in Fig.~\ref{fig_comparison_concept} (b).
BorderDet \cite{qiu2020borderdet} designs three types of border-sensitive features (see Fig.~\ref{fig_comparison_concept} (c)-(e)) to facilitate LQE.
Similar with BorderDet,
a star-shaped sampling manner (see Fig.~\ref{fig_comparison_concept} (f))
is designed in VFNet \cite{zhang2020varifocalnet}.
Alternatively, HSD \cite{cao2019hierarchical} and RepPoints \cite{yang2019reppoints,chen2020reppoints} focus on features with learned locations~(see Fig.~\ref{fig_comparison_concept} (g)) via the deformable convolution \cite{dai2017deformable,zhu2019deformable}.

The aforementioned methods mainly focus on extracting discriminating convolutional features with various spatial aspects for better LQE.
Different from previous methods, our proposed GFLV2 is designed in an
artful perspective:
predicting LQE scores by its directly correlated variables---the statistics of bounding box distributions (see the right part of Fig.~\ref{fig_comparison_concept}).
As later demonstrated in
Table~\ref{tab_input_source},
compared with convolutional features shown in Fig.~\ref{fig_comparison_concept} (a)-(g),
the statistics of bounding box distributions achieve an impressive efficiency and a high accuracy simultaneously.

\section{Method}
In this section, we first
briefly review the
Generalized Focal Loss (i.e., GFLV1 \cite{li2020generalized}), and then derive the proposed GFLV2 based on the relevant
concepts and formulations.

\subsection{Generalized Focal Loss V1}
\noindent \textbf{Classification-IoU Joint Representation}:
This representation
is
the key
component in GFLV1, which is designed to
reduce the inconsistency between localization quality estimation and object classification during training and inference.
Concretely,
given an object with category label
$c \in \{1, 2, ..., m\}$
($m$
indicates
the total number of categories),
GFLV1 utilizes the classification branch to produce the joint representation of Classification and IoU as $\textbf{J} = [J_1, J_2, ..., J_m]$, which
satisfies:
\begin{equation}
\begin{split}
J_i =
\left\{
\begin{array}{ll}
{\rm IoU}(b_{pred}, b_{gt}), & {\rm if\ }i=c; \\
0, & {\rm otherwise},
\end{array}
\right.
\end{split}
\end{equation}
where ${\rm IoU}(b_{pred}, b_{gt})$ denotes the IoU between the predict bounding box $b_{pred}$ and the ground truth $b_{gt}$.
\iffalse
\begin{equation}
\setlength{\abovedisplayskip}{2pt}
\setlength{\belowdisplayskip}{2pt}
\begin{aligned}
J_c &= y \\
J_i &= 0, i \ne c.
\end{aligned}
\end{equation}
\fi
\iffalse
To achieve this goal, GFLV1 utilizes Quality Focal Loss (QFL) that enables its successful learning:
\begin{equation}
\setlength{\abovedisplayskip}{3pt}
\setlength{\belowdisplayskip}{3pt}
\textbf{QFL}(\textbf{J}) = -\big|\textbf{y} - \textbf{J}\big|^{\beta}\big((\textbf{1} - \textbf{y})\log(\textbf{1} - \textbf{J}) + \textbf{y}\log(\textbf{J})\big),
\end{equation}
where for label vector $\textbf{y}$ we have $\textbf{y}_c = y$ and $\textbf{y}_i = 0, i \ne c$.
\fi

\noindent \textbf{General Distribution of Bounding Box Representation}:
Modern detectors~\cite{ren2015faster,lin2017focal,tian2019fcos} usually describe the bounding box regression by Dirac delta distribution: $y = \int_{-\infty}^{+\infty} \delta(x - y) x \mathop{}\!\mathrm{d} x$.
Unlike them, GFLV1 introduces a flexible General Distribution $P(x)$ to represent the bounding box, where each edge of the bounding box can be formulated as: $\hat{y} = \int_{-\infty}^{+\infty} P(x) x \mathop{}\!\mathrm{d} x = \int_{y_0}^{y_n} P(x) x \mathop{}\!\mathrm{d} x$, under a predefined output range of $[y_0, y_n]$.
To be compatible with the convolutional networks, the continuous domain is converted into the discrete one, via discretizing the range $[y_0, y_n]$ into a list $[y_0, y_1, ..., y_i, y_{i+1}, ..., y_{n-1}, y_n]$ with even intervals $\varDelta$ ($\varDelta = y_{i+1} - y_{i}, \forall i \in \{0, 1, ..., n-1\}$). As a result, given the discrete distribution property $\sum_{i = 0}^{n} P(y_i) = 1$, the estimated regression value $\hat{y}$ can be presented as:
\begin{equation}
\setlength{\abovedisplayskip}{2pt}
\setlength{\belowdisplayskip}{2pt}
\hat{y} = \sum_{i = 0}^{n} P(y_i) y_i.
\end{equation}

Compared with the Dirac delta distribution, the General Distribution $P(x)$ can faithfully reflect the prediction quality (see Fig.~\ref{fig_concept} (c)-(d)), which is the cornerstone of this work.

\iffalse
To explicitly encourage the high probabilities of values that are close to the target $y$, and force the network to rapidly focus on these values (e.g., $y_i \le y \le y_{i+1}$), Distribution Focal Loss (DFL) is applied for efficiently learning the explicit distributions of bounding boxes:
\begin{equation}
\setlength{\abovedisplayskip}{2pt}
\setlength{\belowdisplayskip}{2pt}
\begin{aligned}
&\textbf{DFL}(P({y}_i), P({y}_{i+1}))=\\
&-\big((y_{i+1} - y)\log(P({y}_i))+(y - y_{i})\log(P({y}_{i+1}))\big).
\end{aligned}
\end{equation}
\fi

\begin{figure*}[t]
	\begin{center}
		\setlength{\fboxrule}{0pt}

		\fbox{\includegraphics[width=0.98\textwidth]{./figs/method_cropped.pdf}}
	\end{center}
	\captionsetup{font={small}}
	\vspace{-20pt}
	\caption{The illustration of the proposed Generalized Focal Loss V2 (GFLV2), where a novel and tiny Distribution-Guided Quality Predictor (DGQP) uses the statistics of learned bounding box distributions to facilitate generating reliable IoU quality estimations.}
	\label{fig_method_cropped}
	\vspace{-10pt}
\end{figure*}
\subsection{Generalized Focal Loss V2}
\label{sec_gflv2}
\noindent
\textbf{Decomposed Classification-IoU Representation}:
Although the joint representation solves
the inconsistency problem~\cite{li2020generalized} between
{object classification}
and
{quality estimation during training and testing,}
there are still some limitations in using
{only}
{the classification branch to predict the joint representation.}
In this work, we decompose the joint representation explicitly by leveraging information from both classification ($\textbf{C}$) and regression ($I$) branches:
\begin{equation}
\setlength{\abovedisplayskip}{2pt}
\setlength{\belowdisplayskip}{2pt}
{\textbf{J} = \textbf{C} \times {I},}
\label{jci}
\end{equation}
where $\textbf{C} = [C_1, C_2, ..., C_m], C_i \in [0, 1]$
denotes the Classification Representation of total $m$ categories, and ${I} \in [0, 1]$ is a scalar that stands for the IoU Representation.

Although $\textbf{J}$ is decomposed into two components,
we use the final joint formulation (i.e., $\textbf{J}$) in both the training and testing phases,
so it can still avoid the inconsistency problem as mentioned in GFLV1.
Specifically,
we first combine $\textbf{C}$ from the classification branch and ${I}$ from the proposed Distribution-Guided Quality Predictor (DGQP) in  regression branch, into the unified form $\textbf{J}$. Then, $\textbf{J}$ is supervised by Quality Focal loss (QFL) as proposed in~\cite{li2020generalized} during training, and used directly as NMS score in inference.

\noindent \textbf{Distribution-Guided Quality Predictor}:
DGQP is the key component of GFLV2.
It delivers the statistics of the learned General Distribution $\mathbf{P}$ into a tiny sub-network (see \textcolor{red}{red} dotted frame in Fig.~\ref{fig_method_cropped}) to obtain the predicted IoU scalar $I$, which helps to generate high-quality Classification-IoU Joint Representation (Eq.~\eqref{jci}).
Following GFLV1 \cite{li2020generalized},
we adopt the relative offsets from the location to the four sides of a bounding box as the regression targets, which are represented by the General Distribution.
For convenience, we mark the left, right, top and bottom sides as $\{l, r, t, b\}$,
and define the discrete
probabilities
of the $w$ side as $\mathbf{P}^w = [P^w(y_0), P^w(y_1), ..., P^w(y_n)]$, where $w \in \{l, r, t, b\}$.

As illustrated in Fig.~\ref{fig_concept}, the flatness of the learned distribution is highly related to the quality of the final detected bounding box, and
some relevant statistics can be used to reflect the flatness of the General Distribution.
As a result, such statistical features have a very strong correlation with the localization quality, which will ease the training difficulty and improves the quality of estimation.
Practically, we recommand to choose the Top-$k$ values along with the mean value
of each distribution vector $\mathbf{P}^w$, and concatenate them as the basic statistical feature $\mathbf{F} \in \mathbb{R}^{4(k+1)}$:
\begin{equation}
\setlength{\abovedisplayskip}{3pt}
\setlength{\belowdisplayskip}{3pt}
\mathbf{F} = {\rm Concat}\big(\big\{{\rm Topkm}(\mathbf{P}^w)\ |\ w \in \{l, r, t, b\}\big\}\big),
\end{equation}
where ${\rm Topkm}(\cdot)$ denotes the joint operation of calculating Top-$k$ values and their mean value. ${\rm Concat}(\cdot)$ means the channel concatenation. Selecting Top-$k$ values and their mean value as the input statistics have two benefits:

$\bullet$ Since the sum of $\mathbf{P}^w$ is fixed (i.e., $\sum_{i=0}^{n}{P}^w(y_i) = 1$), Top-$k$ values along with their mean value can basically reflect the flatness of the distribution: the larger, the sharper; the smaller, the flatter;

$\bullet$ Top-$k$ and mean values can make the statistical feature insensitive to its relative offsets over the distribution domain (see Fig.~\ref{fig_topk_cropped}), resulting in a robust representation which is not affected by object scales.

\begin{figure}[h]
	\vspace{-10pt}
	\begin{center}
		\setlength{\fboxrule}{0pt}

		\fbox{\includegraphics[width=0.48\textwidth]{./figs/topk_cropped.pdf}}
	\end{center}
	\captionsetup{font={small}}
	\vspace{-20pt}
	\caption{$\text{{Topkm}}(\cdot)$ feature is robust to object scales.}
	\label{fig_topk_cropped}
	\vspace{-10pt}
\end{figure}

Given the statistical feature $\mathbf{F}$ of General Distribution as input, we design a very tiny sub-network $\mathcal{F}(\cdot)$ to predict the final IoU quality estimation.
The sub-network has only two Fully-Connected (FC) layers, which are followed by ReLU \cite{krizhevsky2012imagenet} and Sigmoid, respectively. Consequently, the IoU scalar $I$ can be calculated as
\begin{equation}
\setlength{\abovedisplayskip}{2pt}
\setlength{\belowdisplayskip}{2pt}
I = \mathcal{F}(\mathbf{F}) = \sigma(\mathbf{W}_2 \delta(\mathbf{W}_1 \mathbf{F})),
\end{equation}
where $\delta$ and $\sigma$ refer to the ReLU and Sigmoid, respectively. $\mathbf{W}_1 \in \mathbb{R}^{p\times 4(k+1)}$ and $\mathbf{W}_2 \in \mathbb{R}^{1\times p}$. $k$ denotes the Top-$k$ parameter and $p$ is the channel dimension of the hidden layer ($k$ = 4, $p$ = 64 is a typical setting in our experiment).

\noindent \textbf{Complexity}: The overall architecture of GFLV2 is illustrated in Fig.~\ref{fig_method_cropped}. It is worth noting that the DGQP module is very lightweight. First, it only brings
thousands of
additional parameters, which are negligible compared to the number of parameters of the entire detection model.
For example, for the model with ResNet-50 \cite{he2016deep} and FPN \cite{lin2017feature}, the extra parameters of the DGQP module only account for $\sim$0.003\%.
Second,
the computational overhead of the DGQP module is also very small due to its extremely light structure.
As shown in Table~\ref{tab_compatibility} and \ref{tab_efficiency}, the use of the DGQP module hardly reduces the training and inference speed of the original detector in practice.

\begin{table}[t]

	\small
	\centering
	\renewcommand\arraystretch{1.1}
	\newcommand{\tabincell}[2]{\begin{tabular}{@{}#1@{}}#2\end{tabular}}

	\resizebox{0.46\textwidth}{!}{
		\begin{tabular}{c|c|c|c| c c c}
			\hline
			Top-$k$ (4) & Mean & Var & Dim & AP & AP$_{50}$ & AP$_{75}$  \\
			\hline\hline

			$\checkmark$ &              &              & 16 & 40.8 & 58.5 & 44.2\\
			& $\checkmark$ &              & 4  & 40.2 & 58.5 & 43.6\\
			&              & $\checkmark$ & 4  & 40.3 & 58.3 & 43.7\\
			$\checkmark$ & $\checkmark$ &              & 20 & \textbf{41.1} & \textbf{58.8} & \textbf{44.9} \\
			$\checkmark$ &              & $\checkmark$ & 20 & 40.9 & 58.5 & 44.7\\
			$\checkmark$ & $\checkmark$ & $\checkmark$ & 24 & 40.9 & 58.4 & 44.7 \\
			\hline

		\end{tabular}
	}
	\captionsetup{font={small}}
	\vspace{-8pt}
	\caption{Performances of different combinations of the input statistics by fixing $k = 4$ and $p = 64$. ``Mean'' denotes the mean value, ``Var'' denotes the variance number, and ``Dim'' is short for ``Dimension'' that means the total amount of the input channels.}
	\vspace{-10pt}
	\label{tab_input_statistics}
\end{table}

\section{Experiment}

\noindent \textbf{Experimental Settings}: We conduct experiments on COCO benchmark \cite{lin2014microsoft}, where {\tt  trainval35k} with 115K images is used for training and {\tt minival} with 5K images for validation in our ablation study. Besides, we obtain the main results on {\tt test-dev} with 20K images from the evaluation server. All results are produced under mmdetection \cite{chen2019mmdetection} for fair comparisons, where the default hyper-parameters are always adopted. Unless otherwise stated, we apply the standard 1x learning schedule (12 epochs) without multi-scale training for the ablation study, based on ResNet-50 \cite{he2016deep} backbone. The training/testing details follow the descriptions in previous works \cite{li2020generalized,chen2020reppoints}.

\subsection{Ablation Study}
\noindent \textbf{Combination of Input Statistics}: In addition to the pure Top-$k$ values, there are some statistics that may reflect more characteristics of the distributions, such as the mean and variance of these Top-$k$ numbers. Therefore, we conduct experiments to investigate the effect of their combinations as input, by fixing $k = 4$ and $p = 64$. From Table~\ref{tab_input_statistics}, we observe that the Top-$4$ values with their mean number perform best. Therefore, we default to use such a combination as the standard statistical input in the following experiments.

\noindent \textbf{Structure of DGQP (i.e., $k$, $p$)}: We then examine the impact of different parameters of $k$, $p$ in DGQP on the detection performance. Specifically, we report the effect of $k$ and $p$ by fixing one and varying another in Table~\ref{tab_kp}. It is observed that $k = 4, p = 64$ steadily achieves the optimal accuracy among various combinations.

\begin{table}[t]

	\small
	\centering
	\renewcommand\arraystretch{1.1}
	\newcommand{\tabincell}[2]{\begin{tabular}{@{}#1@{}}#2\end{tabular}}

	\resizebox{0.46\textwidth}{!}{
		\begin{tabular}{c|c|c c c|c c c}
			\hline
			$k$ & $p$ & AP & AP$_{50}$ & AP$_{75}$ & AP$_{S}$ & AP$_{M}$ & AP$_{L}$  \\
			\hline\hline
			0  &-- & {40.2} & {58.6} & 43.4 & 23.0 & {44.3} & {53.0} \\\hline
			1  & \multirow{6}{*}{64} & 40.2 & 58.3 & 44.0 & 23.4 & 44.1 & 52.1 \\
			2  & & 40.9 & 58.5 & 44.6 & 23.3 & 44.8 & \textbf{53.5}\\
			3  & & 40.9 & 58.5 & 44.6 & \textbf{24.3} & \textbf{44.9} & 52.3\\
			4  & & \textbf{41.1} & \textbf{58.8} & \textbf{44.9} & {23.5} & \textbf{44.9} & 53.3 \\
			8  & & 41.0 & 58.6 & 44.5 & 23.5 & 44.5 & 53.4 \\
			16 & & 40.8 & 58.5 & 44.4 & 23.4 & 44.2 & 53.1\\
			\hline
			\multirow{6}{*}{4} & 8 & 40.9 & 58.4 & 44.5 & 23.1 & 44.5 & 52.6\\
			  & 16  & 40.8 & 58.3 & 44.1 & 23.3 & 44.6 & 52.0 \\
			  & 32  & 40.9 & 58.7 & 44.3 & 23.1 & 44.6 & 53.2\\
			  & 64  & \textbf{41.1} & \textbf{58.8} & \textbf{44.9} & \textbf{23.5} & \textbf{44.9} & \textbf{53.3} \\
			  & 128 & 40.9 & 58.3 & 44.6 & 23.2 & 44.4 & 52.7 \\
			  & 256 & 40.7 & 58.3 & 44.4 & 23.4 & 44.3 & 52.9\\
			\hline

		\end{tabular}
	}
\captionsetup{font={small}}
	\vspace{-8pt}
	\caption{Performances of various $k, p$ in DGQP. $k = 0$ denotes the baseline version without the usage of DGQP (i.e., GFLV1).}
	\vspace{-6pt}
	\label{tab_kp}
\end{table}

\noindent \textbf{Type of Input Features}: To the best of our knowledge, the proposed DGQP is the first to use the statistics of learned distributions of bounding boxes for the generation of better LQE scores in the literature. Since the input (distribution statistics) and the output (LQE scores) are highly correlated, we speculate that it can be more effective or efficient than ordinary convolutional input proposed in existing methods. Therefore, we fix the hidden layer dimension of DGQP (i.e., $p=64$) and compare our statistical input with most existing possible types of convolutional inputs, from point (a), region (b), border (c)-(e), regular points (f), and deformable points (g), respectively (Fig.~\ref{fig_comparison_concept}). Table~\ref{tab_input_source} shows that our distribution statistics perform best in overall AP, also fastest in inference, compared against various convolutional features.

\begin{table}[t]

	\small
	\centering
	\renewcommand\arraystretch{1.1}
	\newcommand{\tabincell}[2]{\begin{tabular}{@{}#1@{}}#2\end{tabular}}

	\resizebox{0.48\textwidth}{!}{
		\begin{tabular}{c|c| c c c | c}
			\hline
			\multicolumn{2}{c|}{Input Feature} & AP & AP$_{50}$ & AP$_{75}$ & FPS \\
			\hline\hline

			\multicolumn{2}{c|}{ Baseline (ATSS \cite{zhang2019bridging} w/ QFL \cite{li2020generalized})} & {39.9} & {58.5} & {43.0} & \textbf{19.4}\\
			\hline
			\multirow{7}{*}{\tabincell{c}{Convolutional Features}} & (a) &  40.2 & 58.6 & 43.7 & {19.3} \\
			 & (b) & 40.5 & 59.0 & 44.0 & 14.0\\
			 & (c) & 40.5 & 58.7 & 44.1 & 16.2\\
			 & (d) & 40.6 & 59.0 & 44.0 & 18.3\\
			 & (e) & 40.6 & 58.9 & 44.1 & 17.8\\
			 & (f) & 40.7 & 59.0 & 44.1 & 17.9 \\
			 & (g) & 40.8 & 58.9 & 44.6 & 18.4 \\\hline
			\multicolumn{2}{c|}{Distribution Statistics \textbf{(ours)} }  &  \textbf{41.1} & 58.8 & 44.9 & \textbf{19.4}\\
			\hline

		\end{tabular}
	}
\captionsetup{font={small}}
	\vspace{-8pt}
	\caption{Comparisons among different input features by fixing the hidden layer dimension of DGQP.}
	\vspace{-10pt}
	\label{tab_input_source}
\end{table}

\begin{figure}[t]
	\begin{center}
		\setlength{\fboxrule}{0pt}

		\fbox{\includegraphics[width=0.40\textwidth]{./figs/decompose_compare_cropped.pdf}}
	\end{center}
	\captionsetup{font={small}}
	\vspace{-18pt}
	\caption{Different ways to utilize the distribution statistics, including Decomposed Form (left) and Composed Form (right).}
	\label{fig_decompose_compare_cropped}
	\vspace{-10pt}
\end{figure}

\noindent \textbf{Usage of the Decomposed Form}: Next, we examine what is the best formulation of Classification-IoU Joint Representation in the case of using distribution statistics. There are basically two formats: the Composed Form and the proposed Decomposed Form (Sec.~\ref{sec_gflv2}), as illustrated in Fig.~\ref{fig_decompose_compare_cropped}. Here the ``Decomposed'' (the left part of Fig.~\ref{fig_decompose_compare_cropped}) means that the final joint representation can be explicitly decomposed through multiplication by two components, i.e., $\textbf{J} = \textbf{C} \times {I}$ in Eq.~\eqref{jci}. Whilst the ``Composed'' (the right part of Fig.~\ref{fig_decompose_compare_cropped}) shows that $\textbf{J}$ is directly obtained through FC layers where its input feature is enriched ($d$ is the dimension of the appended feature) by the information of distribution statistics. From Table~\ref{tab_decompose_compare}, our proposed Decomposed Form is always superior than the Composed Forms with various $d$ settings in both accuracy and running speed.

\begin{table}[t]

	\small
	\centering
	\renewcommand\arraystretch{1.1}
	\newcommand{\tabincell}[2]{\begin{tabular}{@{}#1@{}}#2\end{tabular}}

	\resizebox{0.46\textwidth}{!}{
		\begin{tabular}{c|l|c c c|c}
			\hline
			\multicolumn{2}{c|}{ Type} & AP & AP$_{50}$ & AP$_{75}$ & FPS \\
			\hline\hline
			\multicolumn{2}{c|}{ Baseline (GFLV1 \cite{li2020generalized}) } & {40.2} & {58.6} & 43.4 & \textbf{19.4} \\
			\hline
			\multirow{5}{*}{Composed Form} & $d=16$  & 40.5 & 58.5 & 43.7 & 19.2 \\
										   & $d=32$  & 40.5 & 58.5 & 43.7 & 19.2 \\
										   & $d=64$  & 40.7 & 58.5 & 44.3 & 19.1 \\
										   & $d=128$ & 40.7 & 58.6 & 44.4 & 18.9 \\
										   & $d=256$ & 40.7 & 58.3 & 44.2 & 18.5 \\\hline
			\multicolumn{2}{c|}{Decomposed Form \textbf{(ours)}} & \textbf{41.1} & \textbf{58.8} & \textbf{44.9} & \textbf{19.4} \\
			\hline

		\end{tabular}
	}
\captionsetup{font={small}}
	\vspace{-8pt}
	\caption{Comparisons between Decomposed Form (proposed) and Composed Form (with various dimension $d$ settings).}
	\vspace{-6pt}
	\label{tab_decompose_compare}
\end{table}

\noindent \textbf{Compatibility for Dense Detectors}: Since GFLV2 is very lightweight and can be adapted to various types of dense detectors, we employ it to a series of recent popular detection methods. For those detectors that do not support the distributed representation of bounding boxes, we make the minimal and necessary modifications to enable it to generate distributions for each edge of a bounding box.
Based on the results in Table~\ref{tab_compatibility}, GFLV2 can consistently improve $\sim$2 AP in popular dense detectors, without loss of inference speed.

\begin{table}[t]

	\small
	\centering
	\renewcommand\arraystretch{1.1}
	\newcommand{\tabincell}[2]{\begin{tabular}{@{}#1@{}}#2\end{tabular}}

	\resizebox{0.48\textwidth}{!}{
		\begin{tabular}{c|c|l c c|c}
			\hline
			Method & GFLV2 & AP & AP$_{50}$ & AP$_{75}$ & FPS \\
			\hline\hline
			RetinaNet \cite{lin2017focal} & & 36.5 & 55.5 & 38.7 & 19.0\\
			RetinaNet \cite{lin2017focal}& $\checkmark$ & \textbf{38.6 {\scriptsize(+2.1)}} & \textbf{56.2} & \textbf{41.7} & 19.0\\
			\hline
			FoveaNet \cite{kong2019foveabox}& & 36.4 & 55.8 & 38.8 & 20.0\\
			FoveaNet \cite{kong2019foveabox}& $\checkmark$ & \textbf{38.5 {\scriptsize(+2.1)}} & \textbf{56.8} & \textbf{41.6} & 20.0\\
			\hline
			FCOS \cite{tian2019fcos} & & 38.5 & 56.9 & 41.4 & 19.4 \\
			FCOS \cite{tian2019fcos} & $\checkmark$ & \textbf{40.6 {\scriptsize(+2.1)}} & \textbf{58.2} & \textbf{43.9} & 19.4\\
			\hline
			ATSS \cite{zhang2019bridging}& & 39.2 & 57.4 & 42.2 & 19.4 \\
			ATSS \cite{zhang2019bridging}& $\checkmark$ & \textbf{41.1 {\scriptsize(+1.9)}} & \textbf{58.8} & \textbf{44.9} & 19.4\\
			\hline

		\end{tabular}
	}
\captionsetup{font={small}}
	\vspace{-8pt}
	\caption{Integrating GFLV2 into various popular dense object detectors. A consistent $\sim$2 AP gain is observed without loss of inference speed.}
	\vspace{-6pt}
	\label{tab_compatibility}
\end{table}

\begin{figure}[t]
    \vspace{-8pt}
	\begin{center}
		\setlength{\fboxrule}{0pt}

		\fbox{\includegraphics[width=0.42\textwidth]{./figs/gfl_curve.pdf}}
	\end{center}

	\vspace{-24pt}
	\captionsetup{font={small}}
	\caption{Single-model single-scale speed (ms) vs. accuracy (AP) on COCO {\tt test-dev} among state-of-the-art approaches. GFLV2 achieves better speed-accuracy trade-off than its competitive counterparts.}
	\label{fig_performance}
	\vspace{-6pt}
\end{figure}

\subsection{Comparisons with State-of-the-arts}
In this section, we compare GFLV2 with state-of-the-art approaches on COCO {\tt test-dev} in Table~\ref{tab_sota}. Following previous works \cite{lin2017focal,tian2019fcos}, the multi-scale  ([480, 960]) training strategy and 2x learning schedule (24 epochs) are adopted during training. For a fair comparison, the results of single-model single-scale testing for all methods are reported, including their corresponding inference speeds (FPS). We also report additional multi-scale testing results for GFLV2. The visualizaion of the accuracy-speed trade-off is demonstrated in Fig.~\ref{fig_performance}, and we observe that GFLV2 pushes the envelope of accuracy-speed boundary to a new level. Our best result with a single Res2Net-101-DCN model achieves considerably competitive 53.3 AP.
\begin{table}[t]

	\small
	\centering
	\renewcommand\arraystretch{1.1}
	\newcommand{\tabincell}[2]{\begin{tabular}{@{}#1@{}}#2\end{tabular}}

	\resizebox{0.4\textwidth}{!}{
		\begin{tabular}{l|l|c|l}
			\hline
			Method & AP & FPS & \tabincell{c}{PCC} $\uparrow$  \\
			\hline\hline
			FCOS$^*$ \cite{tian2019fcos}  & 39.1 & 19.4 & 0.624 \\
			ATSS$^*$ \cite{zhang2019bridging} & 39.9 & 19.4 & 0.631 \\\hline
			GFLV1    \cite{li2020generalized} & 40.2 & 19.4 & 0.634 \\

			GFLV2 \textbf{(ours)} & \textbf{41.1 {\scriptsize(+0.9)}} & 19.4 & \textbf{0.660 {\scriptsize(+0.26)}} \\
			\hline

		\end{tabular}
	}
	\captionsetup{font={small}}
	\vspace{-8pt}
	\caption{Pearson Correlation Coefficients (PCC) for representative dense object detectors. $^*$ denotes the application of Classification-IoU Joint Representation, instead of additional \emph{Centerness} branch. }
	\vspace{-10pt}
	\label{tab_error}
\end{table}

\begin{table*}[t]
	\renewcommand\arraystretch{1.2}

	\centering

	\footnotesize \setlength{\tabcolsep}{4.5pt}
	\vspace{-2pt}
	\begin{threeparttable}
		\resizebox{\textwidth}{!}{
			\begin{tabular}{l|l|c|c|c|ccc|ccc|c}
				\hline
				Method  & Backbone & Epoch & MS$_{\text{train}}$ & FPS & AP &AP$_{50}$ &AP$_{75}$ &AP$_{\it S}$ &AP$_{\it M}$ &AP$_{\it L}$ & Reference\\
				\hline
				\textit{multi-stage:}  & & & & & & & & & & & \\

				Faster R-CNN w/ FPN \cite{lin2017feature}  & R-101 & 24 & & 14.2 & 36.2 & 59.1 & 39.0 & 18.2 & 39.0 & 48.2 & CVPR17 \\

				Cascade R-CNN \cite{cai2018cascade}  & R-101 & 18 & & 11.9 &42.8 &62.1 &46.3 &23.7 &45.5 &55.2 & CVPR18\\
				Grid R-CNN \cite{lu2019grid} & R-101 & 20 &  & 11.4 &41.5 & 60.9 & 44.5 & 23.3 & 44.9 & 53.1 & CVPR19 \\

				Libra R-CNN \cite{pang2019libra} & X-101-64x4d & 12 & & 8.5 & 43.0 & 64.0 & 47.0 & 25.3 & 45.6 & 54.6 & CVPR19\\
				RepPoints \cite{yang2019reppoints}   &R-101 & 24 & & 13.3 & 41.0 & 62.9 & 44.3 & 23.6 & 44.1 & 51.7 & ICCV19\\
				RepPoints \cite{yang2019reppoints}   &R-101-DCN& 24 & $\checkmark$ & 11.8 &
				45.0 & 66.1 & 49.0 & 26.6 & 48.6 & 57.5 & ICCV19 \\
				RepPointsV2 \cite{chen2020reppoints} & R-101 & 24 & $\checkmark$ & 11.1 & 46.0 & 65.3 & 49.5 & 27.4 & 48.9 & 57.3 & NeurIPS20 \\
				RepPointsV2 \cite{chen2020reppoints} & R-101-DCN & 24 & $\checkmark$ & 10.0 &  48.1 & 67.5 & 51.8 & 28.7 & 50.9 & 60.8 & NeurIPS20 \\
				TridentNet \cite{li2019scale}& R-101 & 24 & $\checkmark$ & 2.7$^*$ & 42.7 & 63.6 & 46.5 & 23.9 & 46.6 & 56.6 & ICCV19 \\
				TridentNet \cite{li2019scale}& R-101-DCN & 36 & $\checkmark$ & 1.3$^*$ & 46.8 & 67.6 & 51.5 & 28.0 & 51.2 & 60.5 & ICCV19 \\
				TSD \cite{song2020revisiting} & R-101 & 20 &  & 1.1 & 43.2 & 64.0 & 46.9 & 24.0 & 46.3 & 55.8 & CVPR20\\

				BorderDet \cite{qiu2020borderdet} & R-101 & 24 & $\checkmark$ & 13.2$^*$ &  45.4 & 64.1 & 48.8 & 26.7 & 48.3 & 56.5 & ECCV20\\
				BorderDet \cite{qiu2020borderdet} & X-101-64x4d & 24 & $\checkmark$ & 8.1$^*$ &  47.2 & 66.1 & 51.0 & 28.1 & 50.2 & 59.9 & ECCV20\\
				BorderDet \cite{qiu2020borderdet} & X-101-64x4d-DCN & 24 & $\checkmark$ & 6.4$^*$ &  48.0 & 67.1 & 52.1 & 29.4 & 50.7 & 60.5 & ECCV20\\
				\hline
				\hline
				\textit{one-stage:}   & & & & & & & & & & & \\
				CornerNet \cite{law2018cornernet} &HG-104 & 200 & $\checkmark$ & 3.1$^*$ & 40.6 & 56.4 & 43.2 & 19.1 & 42.8 & 54.3 & ECCV18 \\

				CenterNet \cite{duan2019centernet} &HG-104 & 190 &  $\checkmark$ & 3.3$^*$ &44.9 &62.4 &48.1 &25.6 &47.4 &57.4 & ICCV19 \\
				CentripetalNet \cite{dong2020centripetalnet} & HG-104 & 210 & $\checkmark$ & n/a  & 45.8 & 63.0 & 49.3 & 25.0 & 48.2 & 58.7 & CVPR20\\

				RetinaNet \cite{lin2017focal} &R-101 &  18 &  & 13.6 &39.1 &59.1 &42.3 &21.8 &42.7 &50.2 & ICCV17 \\
				FreeAnchor \cite{zhang2019freeanchor} & R-101 & 24 & $\checkmark$ & 12.8 & 43.1 & 62.2 & 46.4 & 24.5 & 46.1 & 54.8 & NeurIPS19 \\
				FreeAnchor \cite{zhang2019freeanchor} & X-101-32x8d & 24 & $\checkmark$ & 8.2 & 44.9 & 64.3 & 48.5 & 26.8 & 48.3 & 55.9 & NeurIPS19 \\

				FSAF \cite{zhu2019feature} & R-101 & 18 & $\checkmark$ & 15.1 & 40.9 & 61.5 & 44.0 & 24.0 & 44.2 & 51.3 & CVPR19\\
				FSAF \cite{zhu2019feature}& X-101-64x4d & 18 & $\checkmark$ & 9.1 & 42.9 & 63.8 & 46.3 & 26.6 & 46.2 & 52.7 & CVPR19 \\
				FCOS \cite{tian2019fcos} & R-101 & 24 & $\checkmark$ & 14.7 &41.5 & 60.7 & 45.0 & 24.4 & 44.8 & 51.6 & ICCV19\\
				FCOS \cite{tian2019fcos} & X-101-64x4d & 24 & $\checkmark$ & 8.9 & 44.7 & 64.1 & 48.4 & 27.6 & 47.5 & 55.6 & ICCV19\\
				SAPD \cite{zhu2019soft}& R-101 & 24 & $\checkmark$ & 13.2 & 43.5 & 63.6 & 46.5 & 24.9 & 46.8 & 54.6 & CVPR20 \\

				SAPD \cite{zhu2019soft}&  R-101-DCN & 24 & $\checkmark$ & 11.1 & 46.0 & 65.9 & 49.6 & 26.3 & 49.2 & 59.6 & CVPR20 \\
				SAPD \cite{zhu2019soft}& X-101-32x4d-DCN & 24 & $\checkmark$ & 8.8 & 46.6 & 66.6 & 50.0 & 27.3 & 49.7 & 60.7 & CVPR20 \\
				ATSS \cite{zhang2019bridging}& R-101 & 24 & $\checkmark$ & 14.6 &43.6 &62.1 &47.4 &26.1 &47.0 &53.6 & CVPR20 \\

				ATSS \cite{zhang2019bridging} &R-101-DCN & 24 &  $\checkmark$ & 12.7 & 46.3 &64.7 &50.4 &27.7 &49.8 &58.4 & CVPR20\\
				ATSS \cite{zhang2019bridging}& X-101-32x8d-DCN & 24 & $\checkmark$ & 6.9 & {47.7} & {66.6} &{52.1} & {29.3} &50.8 & {59.7} & CVPR20 \\
				PAA \cite{kim2020probabilistic} & R-101 & 24 & $\checkmark$ & 14.6 & 44.8 & 63.3 & 48.7 & 26.5 & 48.8 & 56.3 & ECCV20\\
				PAA \cite{kim2020probabilistic} & R-101-DCN & 24 & $\checkmark$ & 12.7 & 47.4 & 65.7 & 51.6 & 27.9 & 51.3 & 60.6 & ECCV20\\
				PAA \cite{kim2020probabilistic} & X-101-64x4d-DCN & 24 & $\checkmark$ & 6.9 & 49.0 & 67.8 & 53.3 & 30.2 & 52.8 & 62.2 & ECCV20\\
				GFLV1 \cite{li2020generalized} &R-50 & 24 & $\checkmark$ & 19.4 & 43.1 & 62.0 & 46.8 & 26.0 & 46.7 & 52.3 & NeurIPS20\\
				GFLV1 \cite{li2020generalized}&R-101 & 24 & $\checkmark$ & 14.6 & 45.0 & 63.7 & 48.9 & 27.2 & 48.8 & 54.5 & NeurIPS20 \\

				GFLV1 \cite{li2020generalized}&R-101-DCN & 24 & $\checkmark$ & 12.7 & 47.3 & 66.3 & 51.4 & 28.0 & {51.1} & 59.2& NeurIPS20 \\
				GFLV1 \cite{li2020generalized}&X-101-32x4d-DCN & 24 &  $\checkmark$ & 10.7 & {48.2} & {67.4} & {52.6} & {29.2} & {51.7} & {60.2}& NeurIPS20 \\
				\hline
				GFLV2 \textbf{(ours)}&R-50 & 24 & $\checkmark$ & 19.4 & 44.3 & 62.3 & 48.5 &  26.8 & 47.7 & 54.1 & -- \\
				GFLV2 \textbf{(ours)}&R-101 & 24 & $\checkmark$ & 14.6 & 46.2 & 64.3 & 50.5 & 27.8 & 49.9 & 57.0 & -- \\

				GFLV2 \textbf{(ours)}&R-101-DCN & 24 & $\checkmark$ & 12.7 & 48.3 & 66.5 & 52.8 & 28.8 & 51.9 & 60.7 & -- \\
				GFLV2 \textbf{(ours)}&X-101-32x4d-DCN & 24 &  $\checkmark$ & 10.7 & 49.0 & 67.6 & 53.5 & 29.7 & 52.4 & 61.4 & -- \\
				GFLV2 \textbf{(ours)}&R2-101-DCN & 24 &  $\checkmark$ & 10.9 & 50.6 & 69.0 & 55.3 & 31.3 & 54.3 & 63.5 & -- \\
				GFLV2 \textbf{(ours)} + MS$_{\text{test}}$ &R2-101-DCN & 24 &  $\checkmark$ & -- & 53.3 & 70.9 & 59.2 & 35.7 & 56.1 & 65.6 & -- \\
				\hline
			\end{tabular}
		}
	\end{threeparttable}
    \captionsetup{font={small}}
	\vspace{-8pt}
	\caption{Comparisons between state-of-the-art detectors \emph{(single-model and single-scale results except the last row)} on COCO {\tt test-dev}. ``MS$_{\text{train}}$'' and ``MS$_{\text{test}}$'' denote multi-scale training and testing, respectively. FPS values with $^*$ are from \cite{zhu2019soft} or their official repositories \cite{qiu2020borderdet}, while others are measured on the same machine with a single GeForce RTX 2080Ti GPU under the same mmdetection \cite{chen2019mmdetection} framework, using a batch size of 1 whenever possible. ``n/a'' means that both trained models and timing results from original papers are not available. \textbf{R}: ResNet \cite{he2016deep}. \textbf{X}: ResNeXt \cite{xie2017aggregated}. \textbf{HG}: Hourglass \cite{newell2016stacked}. \textbf{DCN}: Deformable Convolutional Network \cite{zhu2019deformable}. \textbf{R2}: Res2Net \cite{gao2019res2net}.}
	\vspace{-6pt}
	\label{tab_sota}
\end{table*}

\begin{figure*}[t]
	\begin{center}
		\setlength{\fboxrule}{0pt}

		\fbox{\includegraphics[width=\textwidth]{./figs/cat_cropped.pdf}}
	\end{center}
	\captionsetup{font={small}}
	\vspace{-22pt}
	\caption{Visualization of predicted bounding boxes before and after NMS, along with their corresponding predicted LQE scores (only Top-4 scores are plotted for a better view). For many existing approaches \cite{tian2019fcos,zhang2019bridging,li2020generalized}, they fail to produce the highest LQE scores for the best candidates. In contrast, our GFLV2 reliably assigns larger quality scores for those real high-quality ones, thus reducing the risk of mistaken suppression in NMS processing. White: ground-truth bounding boxes; Other colors: predicted bounding boxes.}
	\label{fig_cat}
	\vspace{-10pt}
\end{figure*}

\subsection{Analysis}
Although the proposed DGQP module has been shown to improve the performance of dense object detectors, we would also like to understand how its mechanism operates.

\noindent \textbf{DGQP Improves LQE}: To assess whether DGQP is able to benefit the estimation of localization quality, we first obtain the predicted IoUs (given by four representative models with IoU as the quality estimation targets) and their corresponding real IoUs over all the positive samples on COCO {\tt minival}. Then we calculate their Pearson Correlation Coefficient (PCC) in Table~\ref{tab_error}. It demonstrates that DGQP in GFLV2 indeed improves the linear correlation between the estimated IoUs and the ground-truth ones by a considerable margin (+0.26) against GFLV1, which eventually leads to an absolute 0.9 AP gain.

\noindent \textbf{DGQP Eases the Learning Difficulty}: Fig.~\ref{fig_loss_iou} provides the visualization of the training losses on LQE scores, where DGQP in GFLV2 successfully accelerates the training process and converges to lower losses.
\begin{figure}[t]
	\vspace{-6pt}
	\begin{center}
		\setlength{\fboxrule}{0pt}

		\fbox{\includegraphics[width=0.44\textwidth]{./figs/loss_iou.pdf}}
	\end{center}
	\captionsetup{font={small}}
	\vspace{-24pt}
	\caption{Comparisons of losses on LQE between GFLV1 and GFLV2. DGQP helps to ease the learning difficulty with lower losses during training.}
	\label{fig_loss_iou}
	\vspace{-12pt}
\end{figure}

\noindent \textbf{Visualization on Inputs/Outputs of DGQP}: To study the behavior of DGQP, we plot its inputs and corresponding outputs in Fig.~\ref{fig_analysis_subnetwork}. For a better view, we select the mean Top-1 values to represent the input statistics. It is observed that the outputs are highly correlated with inputs as expected.

\begin{figure}[t]
	\vspace{-6pt}
	\begin{center}
		\setlength{\fboxrule}{0pt}

		\fbox{\includegraphics[width=0.28\textwidth]{./figs/DGQP.pdf}}
	\end{center}
	\captionsetup{font={small}}
	\vspace{-22pt}
	\caption{Visualization of the input Top-1 values (mean of four sides) from the learned distribution and the output IoU quality prediction given by DGQP.}
	%
	\label{fig_analysis_subnetwork}
	\vspace{-8pt}
\end{figure}

\noindent \textbf{Training/Inference Efficiency}: We also compare the training and inference efficiency among recent state-of-the-art dense detectors in Table~\ref{tab_efficiency}. Note that PAA \cite{kim2020probabilistic}, RepPointsV2 \cite{chen2020reppoints} and BorderDet \cite{qiu2020borderdet} bring an inevitable time overhead (52\%, 65\%, and 22\% respectively) during training, and the latter two also sacrifice inference speed by 30\% and 14\%, respectively. In contrast, our proposed GFLV2 can achieve top performance ($\sim$41 AP) while still maintaining the training and inference efficiency.

\begin{table}

	\small
	\centering
	\renewcommand\arraystretch{1.1}
	\newcommand{\tabincell}[2]{\begin{tabular}{@{}#1@{}}#2\end{tabular}}

	\resizebox{0.49\textwidth}{!}{
		\begin{tabular}{l|c|c|c}
			\hline
			Method & AP & Training Hours $\downarrow$ & Inference FPS $\uparrow$ \\
			\hline\hline

			ATSS$^*$ \cite{zhang2019bridging} & 39.9 &  \ \ 8.2 & 19.4  \\
			GFLV1    \cite{li2020generalized} & 40.2 &  \ \ 8.2 & {19.4}  \\
			PAA       \cite{kim2020probabilistic} & 40.4 & \ \ \ \ \ \ \ \ \ \ \  12.5 \textcolor{red}{\scriptsize (+52\%)} & {19.4}  \\% 0.520 /
			RepPointsV2 \cite{chen2020reppoints} & 41.0 & \ \ \ \ \ \ \ \ \ \ \ 14.4  \textcolor{red}{\scriptsize (+65\%)} &  \ \ \ \ \ \ \ \ \ \    13.5 \textcolor{red}{\scriptsize (-30\%)}  \\
			BorderDet \cite{qiu2020borderdet} & 41.4 & \ \ \ \ \ \ \ \ \ \ \ 10.0 \textcolor{red}{\scriptsize (+22\%)} & \ \ \ \ \ \ \ \ \ \  16.7 \textcolor{red}{\scriptsize (-14\%)} \\
			\hline
			GFLV2 \textbf{(ours)} & 41.1 &  \ \ 8.2 & {19.4} \\
			\hline

		\end{tabular}
	}
\captionsetup{font={small}}
	\vspace{-8pt}
	\caption{Comparisons of training and inference efficiency based on ResNet-50 backbone. ``Training Hours'' is evaluated on 8 GeForce RTX 2080Ti GPUs under standard 1x schedule (12 epochs). $^*$ denotes the application of Classification-IoU Joint Representation.}
	\vspace{-12pt}
	\label{tab_efficiency}
\end{table}

\noindent \textbf{Qualitative Results}: In Fig.~\ref{fig_cat}, we qualitatively demonstrate the mechanism how GFLV2 makes use of its more reliable IoU quality estimations to maintain accurate predictions during NMS. Unfortunately for other detectors, high-quality candidates are wrongly suppressed due to their relatively lower localization confidences, which eventually leads to a performance degradation.

\section{Conclusion}
In this paper, we propose to learn reliable localization quality estimation, through the guidance of statistics of bounding box distributions. It is an entirely new and completely different perspective in the literature, which is also conceptually effective as the information of distribution is highly correlated to the real localization quality. Based on it we develop a dense object detector, namely GFLV2. Extensive experiments and analyses on COCO dataset further validate its effectiveness, compatibility and efficiency. We hope GFLV2 can serve as simple yet effective baseline for the community.

{\small
\bibliographystyle{ieee_fullname}

redpajama_set_name: RedPajamaArXiv


text:
Streetscape plans electrical work
By Wayne Clark
Published 8:01 am Wednesday, April 4, 2018
LANETT — As part of the downtown streetscape project, some of the overhead electrical lines on North Lanier Avenue will be going underground. By the end of Phases I and II of the project this should be taken care of, and there will be no overhead lines on the portion of North Lanier that runs from the downtown triangle to the bridge over Tanyard Creek, just past city hall.
To have this "line-free" zone, some downtown businesses will be receiving their electricity from behind their buildings rather than in front.
Some new overhead lines will run from First Street along a back alley to do this. This will also involve the moving of some electrical boxes and for the businesses to be reconnected.
On Monday, the Lanett City Council approved a resolution to do this at no cost to the affected businesses.
"Our goal is to get everything underground that we can," said Mayor Kyle McCoy. "The problem with Phase III (from the creek to the state line) is that much of it is in a flood zone, and we can't have underground lines in these locations. We could put up some new poles to make it more attractive."
In other action at Monday's council meeting, engineering services for a new walking trail were awarded to Goodwyn, Mills and Cawood of Montgomery. A grant of approximately $41,000 has been approved to build this new trail on the middle terrace of the W.O. Lance Elementary School playground. Plans call for an open pavilion, a slide from the middle to the bottom terrace, some wooden fencing along the top of the bottom terrace, park bench seating and lighting for the night-time hours.
City officials are considering the possibility of having a splash pad on the bottom section next to South 6th Avenue. An outdoor basketball court would remain.
In a unanimous vote, the council appointed Martha Scott to the Chambers County Development Authority Board of Directors. Council Member Tony Malone thanked her for agreeing to serve.
The council approved an off premise beer and wine license for the Summit Big Cat store located inside the police jurisdiction on Veterans Memorial Parkway. This was needed because the business has been sold from one party to another.
"We had a tremendous turnout at our egg hunt last Thursday at Morgan-Washburn Stadium," McCoy said. "The weather was good, and I think everyone had a good time."
The mayor thanked Chambers County Commissioners Sam Bradford and Debbie Wood for being in attendance at the meeting and thanked them for what they do for Lanett and the county.
Wood said she was most impressed with the progress of the streetscape project.
"It's really beautiful," she said.
BRIEFLY TOLD
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Key Enablers [ PDF 130KB]
The Integrated Investment Program will guide approximately $195 billion of investment to 2025‐26 to deliver the future force of the Defence White Paper. The Government will invest around 25 per cent of Defence capital expenditure to 2025‐26 to remediate the underinvestment of recent years in those key enablers essential to realising the full potential of the high technology systems entering service. The Government's plans recognise that even the most capable platforms and systems cannot be effective without the enabling capabilities that allow those platforms to operate effectively and be sustained. Enabling capabilities include our people, critical infrastructure such as bases, training ranges, wharves and airfields, information and communications technology, logistics, science and technology, and health services, as well as force design, strategic and international policy functions.
Key Enabling Capability enhancements
Reliable access to essential facilities such as military bases, wharves, port facilities, airbases, training ranges and fuel and explosive ordnance infrastructure underpins the Australian Defence Force's (ADF) ability to conduct and sustain operations in Australia and into the region. We will implement a range of Defence infrastructure initiatives over the next decade to support future ADF operations.
Royal Australian Air Force (RAAF) bases Williamtown, New South Wales; Edinburgh, South Australia; Tindal and Darwin, Northern Territory; Curtin, Learmonth, and Pearce in Western Australia; and Townsville and Scherger in Queensland and HMAS Stirling, Western Australia; and Defence Establishment Myambat, New South Wales; will be upgraded to support the ADF's strike, air combat and maritime surveillance capabilities.
New facilities at RAAF bases Tindal and Edinburgh will support the introduction of the MQ‐4C Triton unmanned surveillance aircraft in the early 2020s.
The airfield at Cocos (Keeling) Islands will be upgraded to support the new P‐8A Poseidon maritime surveillance and response aircraft.
HMAS Stirling and Fleet Base East (Garden Island) in New South Wales will be upgraded as part of the significant regeneration of the Royal Australian Navy. Garden Island will be upgraded to enable it to continue to support an expanded fleet and accommodate larger platforms such as the Canberra Class amphibious ships and Hobart Class air warfare destroyers.
HMAS Coonawarra in the Northern Territory will also be upgraded in the short‐medium term to enhance its operational effectiveness.
Space situational awareness will be enhanced through upgrades at Harold E. Holt Communications Facility, Exmouth, Western Australia.
Jindalee Operational Radar Network facilities in northern Australia and the communications facility at HMAS Harman in the Australian Capital Territory will be upgraded.
Some additional facilities may be required across existing Army bases such as Darwin, Puckapunyal, Brisbane, and Townsville, and selected weapon ranges and training areas such as Puckapunyal Military Area, Victoria and Shoalwater Bay Training Area, Queensland, to support the new combat reconnaissance and infantry fighting vehicle fleets.
New and enhanced weapons and explosive ordnance facilities at RAAF bases Williamtown, Amberley, Townsville and Tindal and Defence Establishment Orchard Hills in New South Wales will allow new high technology weapon systems to be stored and maintained safely, as will remediation of Point Wilson port infrastructure and Port Wakefield and Graytown proofing and experimentation facilities.
Army bases such as Lavarack Barracks, Queensland; Holsworthy Barracks in New South Wales; Campbell Barracks in Western Australia; and Robertson and Larrakeyah Barracks, Northern Territory, will be upgraded to support Army capability and operations.
Support bases and recruitment training facilities such as HMAS Cerberus in Victoria and Puckapunyal in Victoria will be upgraded to maintain capability.
Shoalwater Bay Training Area ‐ the major ADF joint amphibious training area, Bradshaw Field Training Area, Northern Territory; Cultana Training Area, South Australia; Yampi Sound Training Area, Western Australia; and Puckapunyal Military Area, Victoria, will be upgraded to support ADF capability requirements and increased training with international partners.
Woomera training and testing range in South Australia will be upgraded to meet increasing ADF demands and greater cooperation with the United States and other security partners.
The Government is providing $5 billion in additional funding over the decade to 2025‐26 to modernise Defence's information and communications technology architecture that supports warfighting, security and communications. Priorities will be: enhancing support to operations; stabilising Defence's information and communications technology core; and delivering a rationalised, secure, contemporary information and communications technology environment. Connectivity with allies and partners will be improved, supporting more effective coalition operations.
Simulators, including an enhanced enterprise simulation capability, training platforms, training ranges and testing facilities will also be upgraded to support the new and larger fleets of platforms and new weapons systems being acquired as part of the Government's capability plans.
Around $730 million will be invested to 2025‐26 in targeted next generation technologies which have the potential to deliver game‐changing capabilities critical to Australia's defence and national security.
Health services support for ADF deployments, training and exercises, including jointly with United States forces and other partners will also be enhanced.
An additional around 1,100 ADF positions and around 400 Australian Public Service positions will be allocated to 2025‐26 to support these further investments in our enabling capabilities.


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Q: gdaltools in qgis v2.4? Where's the GDALTools plugin for QGIS 2.4? It's not listed in list of available plugins, and it's seemingly not installed out of the box. Is it just that it's not yet compatible?

A: GDALTools are a core plugin of QGIS, and will be installed by default.
If you are missing some parts of the Vector, Raster or Processing menu, please add a screenshot.

redpajama_set_name: RedPajamaStackExchange


Jumbunna has a number of staff who are qualified Practitioners to present parenting programs.
Triple P (Positive Parenting Program) are run regularly, including the standard, disability, Indigenous specific, and seminar courses. Some are held at night. Child care is usually provided.
The Circle of Security Parenting Program runs for 8 weeks and focuses on strengthening the relationship that parents/carers have with their children. This approach teaches new ways to understand children's needs & behaviour. It offers effective ways to respond to children that will help them to develop a sense of being safe and secure, so that they are able to learn, grow and develop positive relationships with others.
These programs are some of the most well known and most researched early language intervention programs in the world. They are consistent in terms of best practice and using natural context and a family centred approach. Our specially trained and accredited speech pathologist runs these programs.
Brochures are available about these programs at the front office.
This program provides parents of young children with Autism Spectrum Disorder (ASD) or children with social communication difficulties with practical tools to help their children communicate.


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Джала́л Ікрамі́ (також Джалол Ікромі; ; нар. , Бухара, Бухарський емірат, тепер Узбекистан — 1993) — таджицький радянський письменник, класик сучасної таджицької літератури; Народний письменник Таджикистану.

Біографія

Ікромі Джалол народився 7 (20) вересня 1909 в місті Бухарі (тепер Узбекистан) у родині судді.

У 1928 році закінчив учительський Інститут.

1930 року Ікрамі переїхав до Душанбе. Працював науковим співробітником у Комітеті з вивчення Таджикистану, що в подальшому був реорганізований у Науково-дослідницький інститут мови та літератури; співробіником і відповідальним секретарем літературних часописів, завідував літературною частиною Таджицького державного ордену Червоного Прапора академічного театру драми ім. Лахуті; був редактором Таджицького державного видавництва.

Від 1935 року Джалал Ікрамі — член Спілки письменників Таджикистану; від 1945 року — член КПРС.

У повоєнний час Ікрамі був членом Президіуму Правління Спілки письменників Таджикистану, членом редколегії часопису «Памир»; неодноразово обирався депутатом Душанбинської міської Ради депутатів трудящих.

Нагороджений 3 орденами Трудового Червоного Прапора, орденом Дружби народів, орденом «Знак Пошани», медалями і Почесними грамотами Президіуму Верховної Ради Таджицької РСР.

Ікромі Джалол помер у 1993 році і був похований на кладовищі в Лучобі.

У вересні 2009 року в Таджикистані відзначили 100-літній ювілей від дня народження Джалола Ікромі.

Творчість

Перші вірші Джалал Ікрамі опублікував у 1920-х роках.

Визначну роль у письменницькій долі Дж. Ікрамі відіграв С. Айні, який порадив тому випробувати свої сили не в поезії, а у прозі, і який постійно і настійливо вимагав переробляти вже написане та взявся редагувати перші оповідання Ікромі.

Перша збірка оповідань письменника «Життя і перемога» побачила світ у 1934 році.

1962 року було надруковано найбільший і найзначніший твір Ікрамі — роман «Дочка вогню», у якому талановито показане життя Бухари напередодні падіння емірату, у перші роки революції та радянської влади в Бухарі. У 1964 році цей роман був удостоєний Державної премії Таджицької РСР ім. Рудакі.

Теми ранніх оповідань Ікрамі — розкріпачення жінок Сходу, Громадянська війна, викриття лицемірства духовенства.

Джалал Ікрамі — автор повісті «Змія» (1934), драми «Ворог» (1933), комедії «Насіння любові» (1937), романів «Шоді» (ч. 1—2, 1949), у якому подана картина становлення колгоспного устрою в Таджикистані повоєнного часу; «Дочка вогню» (1962) про долю таджицької жінки до революції; «Визнаю себе винним» (1957); трилогії «Дванадцять воріт Бухари» (1962—74).

Ікромі інсценував оповідання «Нещасна дівчина» (1950) і роман «Дохунда» (1954) С. Айні.

Нариси Ікрамі «Герой дніпровського берега», «Від Душанбе до Києва» — про дружбу таджицького і українського народів.

Найповніше чотиритомне зібрання творів Дж. Ікрамі було видано у 1968—73 роках.

Виноски

Джерела та посилання

 Шодикулов Х. Ікрамі Джалол // , стор. 353
 Джалол Ікрам на www.pisateli.freenet.tj (Письменники Таджикистану) // «Писатели Таджикистана», Душанбе: «Ирфон», 1986
 Гоїбназарова Тахміна У столиці вшанували пам'ять Джалола Ікромі  // інф. за 18 вересня 2009 року на www.khovar.tj (Ховар. Національне інформаційне агентство Таджикистану)

Таджицькі письменники
Уродженці Бухари
Персоналії:Душанбе

redpajama_set_name: RedPajamaWikipedia


A man accused of planning a terrorist attack in Sydney was allegedly in possession of hand-written notes referring to an AFP building as a target and a plan to carry out guerrilla warfare in the Blue Mountains.
Counter-terrorism officers arrested Sulayman Khalid, 20, overnight after he was allegedly found with several pages of notes which were designed to facilitate a terrorist attack.
Fairfax Media understands the notes seized by police also contained jihadi rhetoric.
A source has said the notes contained "random" and "crazy" words but did not clearly outline any clear or exact plot.
It is also understood that Mr Khalid has told police the notes do not belong to him.
Mr Khalid, who made an infamous appearance on national television when he stormed off the set of the SBS show Insight, was refused bail by a Sydney court on Wednesday.
A second man who was charged overnight for breaching a terror-related control order, was also refused bail.
Australian Federal Police Deputy Commissioner Michael Phelan said he was confident police had disrupted planned terrorist activity but would not go into details about the documents seized.
"But there was enough there that gave concern to us that something was being planned, and that's why a person was charged," Deputy Commissioner Phelan said.
"He was charged in relation to having a document that was designed to clearly facilitate an attack.
"Certainly, the documents talked a little bit about potential government targets and so on, and what that did, coupled with other things that were seized during the search warrant, gave us significant concern to be able to act early."
"There's certainly nothing that indicates at all about any specific targets or timeframe in relation to this particular activity at all," he said.
Mr Khalid, a part-time labourer from Regents Park, came to prominence when he disrupted the SBS show Insight after he was questioned about the cancellation of his Australian passport.
Lawyer Adam Houda said outside of court that his client had been accused of serious offences.
"My client has been charged with a very serious offence but I remind you that authorities have got it wrong in the past many times and all that we ask is that the court process be respected and for this matter to be determined on the evidence and not by politicians and the media."
A 21-year-old man charged of breaching a control order applied for bail before the same court but was refused.
Only four people have ever been issued with a control order in Australia. One was David Hicks in 2007.
He was allegedly ordered not use public pay phones and prohibited from using mobile phones.
Police allege he made two phone calls which breached the orders – one from a pay phone and one from his mother's mobile.
"He was the subject of a control order made by the court and he directly defied the controls," the prosecution told the court on Wednesday.
"The court could have no confidence in this defendant's behaviour whilst at liberty."
The man, who cannot be named for legal reasons, was detained but not arrested during the Operation Appleby terror raids on September 18, the largest counter-terrorism operation in Australia's history.
Police searched his home and garage last week as part of a series of raids across the city.
Neighbours said they were shocked at the allegations.
He was a "courteous and respectable boy", according to one neighbour who asked not to be named.
"This whole episode has been a complete shock, you just can't describe it. His family have lived here since 2000 and he grew up in the block of units. I don't know what they would be going through."
Neighbours first became aware of the accused's alleged terrorist links when his Marsfield garage and family's unit was raided on September 18.
"Before then I never saw anything suspicious," said another neighbour, who also asked not to be named. "The whole family would say hi in the stairwell. It's just bizarre."
The man and Mr Khalid were arrested by the joint counter-terrorism team overnight as part of Operation Appleby.
In September, more than 800 police officers launched pre-dawn raids across Sydney as part of Operation Appleby.
The Australian intelligence agency put Mr Khalid on a list with 20 other suspects accused of having "an extreme ideology", who were at risk of "engaging in politically motivated violence" if allowed to travel overseas, or had a "jihadi mentality" that made them a threat to national security.
Mr Khalid, who calls himself Abu Bakr after the Prophet Mohammed's father-in-law, also wore an Islamic State flag throughout his appearance on the SBS show Insight.
Mr Khalid said that he sympathised with the values of Islamic State as he was being unfairly targeted by Australians.
"In order for me to be connected to the values here of Australia, the Australian government needs to stop picking on the Muslims here," he said at the time.
"Whenever you express your opinion of a tyrant, you are subjugated to being a terrorist or subjugated to being a national threat."
In September, Mr Khalid pleaded guilty to the intimidation of a cleaner at a Bankstown shopping centre after he threatened a man who objected to his vest, which had an Islamic State flag on it.
The arrests come a day after the Prime Minister, Tony Abbott, said there had been a "heightened level of terror chatter" following the Martin Place siege.
The National Security Committee met on Tuesday and intelligence officials briefed Mr Abbott, who said the national terrorism alert level remained on high.
"As you'll all understand, at this level, an attack is likely, we don't know when and how an attack may come, but we do know there are people with the intent and capability to carry out further attacks, and that's why it's important that as well as being reassured that people are aware of the reality of these times," Mr Abbott said on Tuesday.
Deputy Commissioner Phelan said a total of 11 people had been put before the courts as a result of Operation Appleby.
He estimated there were between 15 and 20 people associated with the group in Sydney, and their ideology was linked to Islamic State.
Police had been monitoring their activity since well before the Martin Place siege last week, he said.
"But certainly during the period following the tragic events of last week, this particular group has continued that activity and that's what gave us, coupled with other events prior to that, enough concern to be able to execute the search warrants last Thursday, sift through that information and then subsequently charge someone last night," he said.
NSW Police Deputy Commissioner Catherine Burn said Operation Appleby was ongoing, and urged the public to go about their activities as normal.
"[Over] Christmas and New Year, people should do what they normally do and feel safe about that," she said.
Mr Khalid will remain behind bars for Christmas and will next appear before Central Local Court on February 18.


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Let's face it. Everyone's lives feel busier and busier these days. With that being said, it can be difficult to keep up with chores and tasks that need to be done around the house. However, failing to take care of small things could lead to very big problems down the road. That's why all home owners should consider using the home maintenance checklist template from Microsoft.
With the home maintenance checklist, all of your maintenance tasks are laid out in a clean, easy-to-read and use format. The checklist also allows you to get things done in a timely manner. You can download and print the home maintenance checklist template instantly for free! Simply click the download link located on this page!
The home maintenance checklist is an Excel spreadsheet created by Microsoft. After downloading, save the home maintenance checklist somewhere on your computer where it will be easy to find. That way, you won't be scrambling to find the template when you need it the most.
The tasks for "Plumbing" include: cleaning the faucets and shower heads, drains, pipes, kitchen and bathroom cabinets, toilets and the water heater. There is a brief description next to each task, as well as a space to note when the last time the task was completed. The entire home maintenance checklist is formatted this way and should be filled out accordingly.
Keep your home looking beautiful and help avoid major potential problems with the home maintenance checklist template from Microsoft. Download the home maintenance checklist template for free today!


text:
Johnson's interview with Yu Jie
July 15, 2012 lijiazhang
China's 'Fault Lines': Yu Jie On His New Biography of Liu Xiaobo
AP Photo/Cliff Owen
Chinese dissident Yu Jie at the National Press Club in Washington, January 18, 2012
Yu Jie is one of China's most prominent essayists and critics, with more than thirty books to his name. His latest work is a biography of his friend, Nobel Peace Prize laureate Liu Xiaobo, that was published in Chinese in Hong Kong a few weeks ago. It is not the first time he has stirred up controversy in China. Yu first gained fame in 1998 at age twenty-five for his book Fire and Ice, a series of biting, satirical essays on contemporary society. Within two years, he was already blacklisted by most publishers. An intensely moralistic person, he also angered many Chinese intellectuals for arguing that they failed to match actions to words.
In 2003, Yu converted to Christianity and increasingly complemented his provocative writing with political activism of his own. He was an early signer of Charter 08, the landmark human rights manifesto, and in 2010 cemented his position as a leading political critic by writing a biography of Chinese Premier Wen Jiabao in which he refers to his subject as "China's best actor." Last year, Yu completed a rough draft of his biography of Liu Xiaobo, who is now serving an eleven-year prison sentence. Authorities warned Yu that he too would be jailed if the book was published and put him under house arrest for several months. In January, he fled China with his wife and son for the United States, where he now resides.
I spoke to Mr. Yu at a church in the Washington area.
Previous Post My meeting with Chen Guangcheng in New York
Next Post a new possibility for China?
One thought on "Johnson's interview with Yu Jie"
Luke Lea says:
Yes, I am particularly interested in Yu's views on Christianity. One of the problems in China, and one of the problems of Marxism in general, is the idea of revenge. Without forgiveness it will never be possible to transition to democracy. Those who control China's wealth are its temporary stewards only. They hold it in trust to the people as a whole and even then only so long as they can manage it efficiently in a competitive economy. Wealth and income should not be taxed, but only consumption. If China can design a progressive or graduated consumption tax that works it will lead the world in that department. Forgiveness and a progressive consumption tax in a free economy — those values will see you through.


text:
Released: January 21, 2016
Yakuza Kiwami Review (PS4)
Yakuza was originally released on the PlayStation 2 in 2005; following a prequel release earlier this year, SEGA has updated the graphics, frame rate and added content clarifying its plot somewhat for its PlayStation 4 upgrade. But is it worth a purchase, for new or returning gamers alike?
Jake Mellor 2017-09-25 No Comments 0 likes
There has been a growing trend towards remakes over the last few years; the Steam and GOG stores are littered with "HD" versions of older games, while the PlayStation, in particular, has seen more than its fair share of remastered classics- Jak and Daxter, Sleeping Dogs and, for some reason, The Legend of Kay. Over the last few years, however, there has been a subtle shift towards remakes- the original Ratchet & Clank, for instance, saw a reworking of the original game, updating the mechanics, characters and weapons to tie in with later instalments, complete with its own surprisingly decent film tie-in. Note: I said decent. Not fantastic.
And now it appears that the Yakuza series is next on the drawing board: Yakuza 0 saw a western release in January this year, and a Kiwami (translation: Extreme) version of Yakuza 2 will hit Japan in time for Christmas. The question is: was the remake worth SEGA's time, and will it successfully attract new and returning players alike? You could be forgiven for calling Yakuza SEGA's Japanese answer to Grand Theft Auto, but in reality, it's rather different to that- at least, it's leagues apart from what we know as GTA today. Yet games like GTA and Sleeping Dogs are likely Yakuza's closest competitors, so comparisons are inevitable.
Yakuza Kiwami is available now on the PlayStation Store for £29.99.
Yakuza Kiwami begins its story in 1995; you play as Yakuza member of the Dojima family, Kazumi Kiryu, who on the eve of him acceding to head his own clan, the Kiryu Clan, decides to cover up for his best friend, Nishikiyami Akira, who murders the head of the Dojima family (their boss) in a fit of rage, after he violates their close friend Sawamura Yumi. As Nishiki has an ill sister in the hospital, our gallant protagonist decides it would be better if he takes the fall, and so lands himself in prison for 10 years, under the pretence of being a murderer (which we may well be, but not for the death we were accused of).
After 10 years of being a goody-two-shoes, Kiryu gets out, only to discover he has been banished from the clan, while 10 billion yen has been stolen from the Tojo Clan's vaults, and their third Chairman, Masaru Sera, has been assassinated, leaving much of the Yakuza in disarray. On top of this, Yumi has gone missing, and Nishiki is a changed man, now head of his own renegade clan. Yeah, it's a lot to take in, even when you're playing. I still have a headache, a week later.
It's not long before Kiryu gets accused of another murder while attending Sera's funeral, despite being sat next to him and the Grandmaster clearly being shot by a sniper through a now-broken window. It gives the lasting impression that most members of the Yakuza seem to be, for wont of a better term, utter dipshits.
The remainder of the game thus primarily consists of figuring out what has happened to the money, Yumi, and your former best friend, who is now somewhat of a psychopath. Flashbacks help to enlighten you on things that happened during your imprisonment, while one of the added features different from the original is that another apparent lunatic, Goro Majima, a rival Yakuza member who appears at random intervals to attack you for no other reason than he seems to be bored. Maybe someone should buy him a PlayStation- or, more preferably, a tranquiliser.
While there is a lot to the story, it actually takes a while to get going fully; the first hour or so of the game largely consists of living your pre-prison life, completing an extortion job (by hitting people over the head with a chair), then buying a birthday present for Yumi (and subsequently attempting to rebuy it, as it is immediately stolen the second you leave the shop, buy a pickpocket with a tendency to hide behind vending machines. Despite spending the vast majority of his life behind soft drink dispensers, he still has time to pawn the ring to a dealer who continues to raise the price each time you enter the store. It's all rather slow and infuriating- you're here to play Yakuza Kiwami, after all, not Yakuza Member Life Simulator.
Eventually, however, the game does open up, and you are free to follow the story at your own pace while completing side missions (called substories), which range from defending yourself against molestation accusations, finding a new pocket racing commentator, and actually listening to a spam caller, for once. (Maybe it's a cultural thing?)
The game is fundamentally an action-adventure game set in an open world; however, the vast majority of the game will be spent in combat, which often comes down to lengthy button-mashing brawls worthy of Street Fighter 2, back when arcades were still a thing. Kiryu has four different fighting styles to master; Beast, Brawler, Rush and Dragon. Each style is suited to different situations and they all have their own advantages and weaknesses; the Brawler style is a more well-rounded combat set, while in Beast mode you sacrifice speed and manoeuvrability for damage. Each style has different moves and abilities that can be unlocked through experience points, with the exception of the Dragon style, which can only be mastered by defeating Majima whenever he appears for his latest instalment of Let's Stop Kiryu From Doing Something Productive.
Each battle begins with its own mini-cutscene reminiscent of games like Tekken or Mortal Kombat, which instead of setting the tone often makes them feel rather like a pantomime, especially when you factor in the giant capital letters that appear on-screen spelling out who you're fighting against (in case you hadn't been paying attention). Fights occur in this game for literally any reason under the sun; defending someone's honour or self-defence against a boss trying to turn you into to mulch is perfectly valid, but the game takes things to ridiculous levels- early on, you can't enter a nightclub without first beating up one of the patrons. In my experience, that's how you exit a club- very swiftly, and very uncooperatively.
There's also a high likelihood of street thugs running over and starting a fight, which generally resorts to mashing the square button as fast as possible to get it over with. It becomes very tiresome, very quickly, and there isn't really a way of avoiding them unless you run quickly away- the game plays almost entirely on foot.
Even the boss fights can get rather repetitive; they are typically enemies with larger health bars and signature moves, which you must successfully dodge or block unless you want your health to disappear faster than a PC gamer's money when the Steam Summer Sale starts. If you struggle with a particularly difficult fight (or are simply crap at the game), you will be presented with the option to temporarily decrease the difficulty, to save at least a fraction of your dignity.
If punching people until they fall over does get slightly tiring, there is a selection of other "weapons" you can use; some are purchasable, like throwing knives, while others are simply conveniently sat next to you when you begin the bout- when it comes to bludgeoning items, there is a veritable smorgasbord- office chairs, billboards, decorative statues- you name it, Kiryu doesn't mind smashing it to pieces over the heads of his enemies.
Thankfully, the game is full of distractions to help break up the story mode, including minigames like pool, darts or, new to Kiwami, pocket car racing (which seems to be the non-proprietary name for Scalextric). The fact that you'll have to fight five gazillion thugs on your way to said entertainment, however, is still infuriating.
The mini-games themselves are certainly amusing distractions; there is a single-player billiards variant, or you can play against an AI player, whose difficulty you can pick; while they're not strictly in-depth, they are solid, and fun to play. You don't always see your opponent, however, which can make victory feel rather hollow, no matter how many cushions you manage to fluke the 9-ball in off.
The game is set in Japan and hence, understandably, everybody speaks Japanese. But whereas many games might, as with anime, replace the original dialogue with a sub-par localised dub, SEGA has maintained the authenticity and feel of the game by retaining the original Japanese audio, and simply adding subtitles, allowing the player to gain some semblance of understanding (although, sans captions, we'd still get the gist of angry members of a Japanese crime syndicate punching people at any given opportunity).
The graphics are, to be blunt, what you'd expect from your average PlayStation 4 game, at most- the original remake was released in Japan for both PS3 and PS4 in January 2016, and it certainly feels more like an upscaling of a PS3 game than anything else. In terms of character animations and overall appearance, there's no effort towards photo-realism, or true-to-life movement as seen in titles like Horizon: Zero Dawn; the extent of characters' expressions are made entirely through their fists.
There also seems to be a tendency for objects in the distance, both inanimate and moving, to appear blurry, giving the distinct sense of Kiryu being myopic. It's not distracting, but it is a little disappointing; it feels more like you are looking through an auto-focusing camera than being present in the moment; it's most prominent when speaking to NPCs, whose faces will be clearly visible, but everything behind them will appear as if looking through a wormhole into the PS2 era. All in all, it's less than breathtaking, and honestly one of the worst facets of the game. Don't get me wrong, though- in no way does the graphical quality limit the experience, just as the original didn't back in 2005.
Yakuza Kiwami adds several new features to expand and improve upon the original release on the PlayStation 2 seventeen years ago; cutscenes help provide context, the graphics have had a complete overhaul, and extra gameplay has been added which should provide enough reason for Yakuza veterans to pick up Kiwami (as in players, not actual crime lords, though I'm certainly not going to stop them buying the game), while new players may well enjoy following Kiryu's journey in a well-crafted and interesting plot almost worthy of a movie- once you get past the mind-numbingly dull hunt for the ring in the prologue. God, that was a drag.
Four free DLC packs have been integrated into recent patches; the extent of these are new outfits, usable items and pocket car parts, however, which puts them in my mind on a par with some of The Witcher 3's DLC packs; a nice touch, but nothing amazing.
The game certainly suits a certain type of player; if you love action-adventure games full of fighting, then Yakuza Kiwami is perfect. If you're a fan of stealing cars, hitting random civilians, robbing stores, listening to the radio while driving around in a stolen sports car, or trying on multiple badly coordinated outfits, then sadly, this is not the game for you. Seriously- in the ten years Kiryu spent in jail, not one person has changed their clothes. It's rather distressing, thinking how unclean they must be, especially with all that spilt blood, so in a way the free DLC is a godsend for your sanity- but only to the extent of Kiryu. Unfortunately, you're stuck with Majima's awful fashion sense for the duration of the entire game.
+ Good story – Slow start
+ Authentic feel, not dubbed – Repetitive gameplay
+ Lots to do with fun mini-games – Limited variety
Earth Defense Force: Iron Rain Review (PS4)
Mortal Kombat 11 Review (PS4)
Rage 2 Review (PS4)
Submersed Review (PS4)


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Brazil's Presidential Elections Worry Global Investment Firms - 2002-05-03
International investors are growing wary of Brazil following negative risk assessments issued this week by several banks and brokerage houses. These negative assessments are based on what might happen in Brazil's presidential race later this year, but some analysts say they are premature.
Brokerage houses Merrill Lynch and Morgan Stanley were the first to revise their risk assessments of Brazil warning their clients this week to reduce their investments in Brazilian government bonds. Two major international banks followed suit - Santander of Spain and ABN AMRO of Holland.
What worries these firms is the possibility that the candidate of Brazil's leftist Workers Party may win next October's presidential election. Opinion polls show candidate Luiz Inacio "Lula" da Silva continuing to maintain a strong lead over his rivals, including the candidate of the government's party, Jose Serra.
The latest national poll showed Mr. da Silva ahead with 38 percent, followed by Mr. Serra with just 16 percent. The survey showed the candidate of the Workers' Party had gained almost six points since March, while support for the government party's candidate fell by three points over the same period.
Analyst Alexandre Barros says investors are worried about a possible victory by Mr. da Silva, even though his party, the PT, has become more moderate.
"Lula and the PT, or a good portion of the PT, are now far less radical than they were ten or twenty years ago," he said. "If you look at the origins of the British Labour Party or the American Democratic Party in the last century they were far more revolutionary and then they evolved, matured, and mellowed so to speak. I think the PT is going pretty much in this direction, and Lula is making an effort to sell this kind of image. The trouble is that the investors are not looking at what he says, they're looking at what he did."
Mr. da Silva, who is making his fourth run for the presidency, dismissed the warnings by the investment banks. He told reporters he will concentrate on developing an economic recovery plan, and not worry about risk assessments adding that Brazil remains economically viable.
Members of his party accused the investment banks of trying to influence the election by scaring investors.
The evaluation of these firms has raised Brazil's risk factor on international markets. Its "risk spread", which is the interest rate premium paid on government bonds, is up by three percent and is at its highest since February.
But analyst Barros, who heads a Brazilian risk analysis firm, Early Warning, says these assessments of Brazil are premature and unwarranted.
"The fact is they're just saying 'beware or be prepared because this may happen'. I don't know how many people are saying 'OK I'm leaving Brazil now,'" he said. "Actually I don't believe the traditional multinationals, which have been in Brazil in many cases for 50 or 60 or more years, and have installations here are going to leave?but the "hot" money people might very well [leave], but I don't think there is any reason for alarm. As a matter of fact there is much more alarm because people are talking about it all the time and because this is making the headlines more than the subject deserves."
Not all investment banks have revised their risk assessments of Brazil. The U.S. bank, J.P. Morgan, Friday issued a positive evaluation of Brazil, saying concerns about opinion polls are exaggerated since campaigning for the presidential election does not officially begin until August.


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Crosscheck Networks to Provide Technology and Business Insights at Infosecurity World Exhibition and Conference
Posted by jmacy on March 7, 2013 | Featured
BOSTON and KUALA LUMPUR, Malaysia, March 7, 2013 /PRNewswire/ —
Crosscheck Networks will exhibit at the Infosecurity World Exhibition and Conference, March 20-21, 2013 at the Putra World Trade Center (Booth A4039) in Kuala Lumpur, Malaysia.
Company executive will present "Defending Multilayer Threats Against Web Services/XML" on Wednesday, March 20, 2013 at 3:45 p.m. MYT.
Crosscheck Networks, a leader in Integrated Software Quality Suites, today announced that it will participate in the Infosecurity World Exhibition and Conference slated for March 20-21, 2013 at the Putra World Trade Center in Kuala Lumpur, Malaysia. Infosecurity World is an annual event that showcases the latest innovations from established and emerging brands in the Asia Pacific information security marketplace.
In addition to the providing hands-on access to Crosscheck Networks' market-leading products (Booth A4039), a senior technical consultant from the company will present "Defending Multilayer Threats Against Web Services/XML" on Wednesday, March 20 at 3:45 p.m. MYT.
Corporate networks and data are becomingly increasingly susceptible to sophisticated multilayer threats. With more organizations embracing Service Oriented Architecture and Cloud Computing, thwarting these new and emerging threats against data delivered over XML has become an increasingly challenging task. Companies such as Forum Systems, a wholly owned subsidiary of Crosscheck Networks, advocate a strategy that uses an XML Gateway, which significantly reduces the complexities IT professionals face when centralizing security, identity and governance across the enterprise.
For more information visit: http://www.infosecurityworld.net/.
Partner Quote
Nattaka Chavavit, Director of Oxfort Technology, Thailand
"Crosscheck Networks' solution in SOA and Cloud addresses overall quality issues and rectifies many security challenges our Thai customers are facing while embarking into the world of SOA. The value of their technologies is easily positioned and justified; the end users genuinely appreciate their technology innovations and support efforts."
Executive Quote
Mamoon Yunus, CEO of Crosscheck Networks and Founder of Forum Systems
"Organizations must remain aware of the constantly changing threats their enterprise's web services face, adopting a proactive strategy for defending their data and systems. We've teamed with companies around the world for nearly a decade to address these risks – ensuring that their Service Oriented Architectures enable enterprise networks to exchange messages over standards like XML in a secure way while defending against increasingly present and damaging multilayer threats."
Learn more about Crosscheck Networks and Forum Systems
Watch the Forum Systems Webinar on SOA governance through the use of gateway technology
Read Forum Systems' Whitepaper on SOA Gateway Deployment
Register to attend the 2013 Infosecurity World Exhibition and Conference
About Crosscheck Networks
Crosscheck Networks and its wholly owned subsidiary, Forum Systems, deliver solutions for deploying robust, resilient, secure and reliable Service Oriented Architecture (SOA). The company's integrated, holistic product suite streamlines and optimizes resources throughout the SOA life cycle, ensuring reduced time-to-market and more rapid return on investment. Recognized as a technology innovator and security leader, Crosscheck Networks is the only company that has been granted a patent for its Forum Sentry XML Gateway and has been certified by NIST and the U.S. Department of Defense. Crosscheck Networks is also the only company with HP Enterprise Management Alliance Program (EMAP) certification for SOA testing with native HP Quality Center Integration. For more information, please visit www.crosschecknet.com.


After the success of their amazing line of Audacious lipsticks, NARS launched their new Audacious Mascara a few months back and I got a deluxe sample when I made an online order at Sephora. Since I am a lover of all things mascara, I was super excited to try this one. I love really black mascaras that are non-waterproof, won't flake, smudge, or is hard to remove at the end of the day.
The mascara comes in a sleek, rubbery tube with a matte finish and has a unique brush that literally has hooks on the red brush. Said to be designed with over 200 molded bristles and lengthening hooks, NARS' lash-catching brush evenly grips and coats lashes from roots to tips while simultaneously separates, lengthens, and amplifies, while the tapered tip finishes off the inner and outermost lashes and enhance the lower lashes. Black Moon Audacious Mascara is a black satin shade that has a wet formula. I like how this mascara is very lengthening and builds really well but it is quite disappointing that it wasn't very volumizing. Remember how I mentioned above that the brush has hooks, well, these bristles are very long and pointy, and if you're not careful, you can bring some serious harm to your eyeballs because the hooks jab at my lash line. The brush however does such a fantastic job of capturing every lash, lengthening them and keeping them separated for a clump free finish and the tip of the brush is tapered to allow easy access to the inner and outer corners of your eyes though I find that it is a little difficult to use the brush on my lower lashes because it is too big. I would also suggest being gentle and taking your time when using this brush.
The mascara separates your lashes and coats them very nicely all the while adding length. With first application I get some length and separation but not volume. After the second and third application, it builds up the volume but just a bit but it provided more length making lashes look full and thick. Although I have short straight eyelashes, I don't use a lash curler but this mascara adds curl well. Black Moon is truly black, with a very natural matte finish. It layers really well with no clumping or flaking and it isn't drying leaving my lashes feeling soft. It's also easy to remove, coming off easily with my cleansing water. Overall rating: 4/5 because of lack of volume and the painful jab I get from the bristles. I won't be picking up the full size either because of the price point.
Find it now at Sephora for US $26.00 / CAD $34.00.


Post subject: where u from?
Hi guys! I'm gonna jump in here. I just joined this forum and I think it's awesome to be able to talk to other teens in a place like this about Pathfinders! I'm from Collegedale, Tennessee. See ya!
I'm from the Metro Detroit area.
I have been ther a coouple of times. I know the directors for the club there.
God bless you, all you.
Last edited by Mitch on Sat Oct 20, 2007 2:23 pm, edited 1 time in total.
Yay for Ellensburg Washington. Hey Mitch, hows i going? Lets hear it for the NW.
YEA< the North West is the best place on earth to have a pathfinder club!!!
I'm doing good Glad u found this awesome place!!!
anyway mitch where are you form in OR i use to live there.
Were in Oregon did you live Chelsa?
Last edited by Mitch on Sat Oct 20, 2007 2:24 pm, edited 1 time in total.
Dallas it is a little town but it is on the map. my friends think it doesnt exsist and some of them are from medford.
I live in North London, England. Which is far from where you guys live!
Cool!! I know where the dallas is... I think it's just those "city slickers" from Portland, Salem and ok Medford that don't know much of Oregon's small towns!
Hey I also know where London England is too!! I went to Germany to visit my uncle who is in the US Airforse. We went to England over Thankgiving weekend... we ended up eating PBJ sandwichs for Thanksgiving lunch and Spicy Indian food for Thanksgiving Dinner...lol It was funny... English don't observe thanksgiving like us Americans do! Don't get me wrong though England is a awesome place...except it was a bit scary to have to drive on the wrong side of the road!
Ok enough... I did go to the Faith on Fire camporee also!! It was an awesome experience!
No, I didn't go 2 faith on fire.
oshkosh was so much fun. that was my second time. i cant wait till the next one.
hi guys i'm from Rusk, Tx. how is it doing my pathfinder friends.