You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
One significant problem of deep-learning based human action recognition is that it can be easily misled by the presence of irrelevant objects or backgrounds. Existing methods commonly address this problem by employing bounding boxes on the target humans as part of the input, in both training and testing stages. This requirement of bounding boxes as part of the input is needed to enable the methods to ignore irrelevant contexts and extract only human features. However, we consider this solution is inefficient, since the bounding boxes might not be available. Hence, instead of using a person bounding box as an input, we introduce a human-mask loss to automatically guide the activations of the feature maps to the target human who is performing the action, and hence suppress the activations of misleading contexts. We propose a multi-task deep learning method that jointly predicts the human action class and human location heatmap. Extensive experiments demonstrate our approach is more robust compared to the baseline methods under the presence of irrelevant misleading contexts. Our method achieves 94.06 and 40.65 (in terms of mAP) on Stanford40 and MPII dataset respectively, which are 3.14 and 12.6 relative improvements over the best results reported in the literature, and thus set new state-of-the-art results. Additionally, unlike some existing methods, we eliminate the requirement of using a person bounding box as an input during testing.

Cited Abstract(s):
[1]: This paper proposes deep convolutional network models that utilize local and global context to make human activity label predictions in still images, achieving state-of-the-art performance on two recent datasets with hundreds of labels each. We use multiple instance learning to handle the lack of supervision on the level of individual person instances, and weighted loss to handle unbalanced training data. Further, we show how specialized features trained on these datasets can be used to improve accuracy on the Visual Question Answering (VQA) task, in the form of multiple choice fill-in-the-blank questions (Visual Madlibs). Specifically, we tackle two types of questions on person activity and person-object relationship and show improvements over generic features trained on the ImageNet classification task

[2]: We present a novel approach to modeling human pose, together with interacting objects, based on compositional models of local visual interactions and their relations. Skeleton models, while flexible enough to capture large articulations, fail to accurately model self-occlusions and interactions. Poselets and Visual Phrases address this limitation, but do so at the expense of requiring a large set of templates. We combine all three approaches with a compositional model that is flexible enough to model detailed articulations but still captures occlusions and object interactions. Unlike much previous work on action classification, we do not assume test images are labeled with a person, and instead present results for "action detection" in an unlabeled image. Notably, for each detection, our model reports back a detailed description including an action label, articulated human pose, object poses, and occlusion flags. We demonstrate that modeling occlusion is crucial for recognizing human-object interactions. We present results on the PASCAL Action Classification challenge that shows our unified model advances the state-of-the-art for detection, action classification, and articulated pose estimation.

[3]:
Holistic methods: Holistic methods extract features from the human in the given bounding box and combine them with contextual features from the whole image to predict human actions [2] [3] [1] . Early works [2] [3] use a graphical model on the human body pose to infer actions. Recently, Mallya and Lazebnik [1] propose a simple fusion network that concatenates features extracted from a bounding box with features from the whole image for action prediction. Overall, holistic methods follow the most straightforward strategy and do not involve many pre-processing steps. However, holistic methods can be easily misled by the presence of irrelevant objects or backgrounds. To resolve this problem, our approach introduces a human-mask loss to guide the activations of the network into human regions, and hence suppresses the response of irrelevant contexts.