You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Feature Encodings and Poolings for Action and Event Recognition: A Comprehensive Survey 
Chapter Title:  Average Pooling 
1. Abstract of Cited Paper (BIB001):  Many modern visual recognition algorithms incorporate a step of spatial 'pooling', where the outputs of several nearby feature detectors are combined into a local or global 'bag of features', in a way that preserves task-related information while removing irrelevant details. Pooling is used to achieve invariance to image transformations, more compact representations, and better robustness to noise and clutter. Several papers have shown that the details of the pooling operation can greatly influence the performance, but studies have so far been purely empirical. In this paper, we show that the reasons underlying the performance of various pooling methods are obscured by several confounding factors, such as the link between the sample cardinality in a spatial pool and the resolution at which low-level features have been extracted. We provide a detailed theoretical analysis of max pooling and average pooling, and give extensive empirical comparisons for object recognition tasks. 
2. Abstract of Cited Paper (BIB002):  Many successful models for scene or object recognition transform low-level descriptors (such as Gabor filter responses, or SIFT descriptors) into richer representations of intermediate complexity. This process can often be broken down into two steps: (1) a coding step, which performs a pointwise transformation of the descriptors into a representation better adapted to the task, and (2) a pooling step, which summarizes the coded features over larger neighborhoods. Several combinations of coding and pooling schemes have been proposed in the literature. The goal of this paper is threefold. We seek to establish the relative importance of each step of mid-level feature extraction through a comprehensive cross evaluation of several types of coding modules (hard and soft vector quantization, sparse coding) and pooling schemes (by taking the average, or the maximum), which obtains state-of-the-art performance or better on several recognition benchmarks. We show how to improve the best performing coding scheme by learning a supervised discriminative dictionary for sparse coding. We provide theoretical and empirical insight into the remarkable performance of max pooling. By teasing apart components shared by modern mid-level feature extractors, our approach aims to facilitate the design of better recognition architectures. 
3. Abstract of Cited Paper (BIB003):  Deep Convolutional Neural Networks (CNNs) are more powerful than Deep Neural Networks (DNN), as they are able to better reduce spectral variation in the input signal. This has also been confirmed experimentally, with CNNs showing improvements in word error rate (WER) between 4-12% relative compared to DNNs across a variety of LVCSR tasks. In this paper, we describe different methods to further improve CNN performance. First, we conduct a deep analysis comparing limited weight sharing and full weight sharing with state-of-the-art features. Second, we apply various pooling strategies that have shown improvements in computer vision to an LVCSR speech task. Third, we introduce a method to effectively incorporate speaker adaptation, namely fMLLR, into log-mel features. Fourth, we introduce an effective strategy to use dropout during Hessian-free sequence training. We find that with these improvements, particularly with fMLLR and dropout, we are able to achieve an additional 2-3% relative improvement in WER on a 50-hour Broadcast News task over our previous best CNN baseline. On a larger 400-hour BN task, we find an additional 4-5% relative improvement over our previous best CNN baseline. 
4. Abstract of Cited Paper (BIB004):  Convolutional Neural Network (CNN) is a biologically inspired trainable architecture that can learn invariant features for a number of applications. In general, CNNs consist of alternating convolutional layers, non-linearity layers and feature pooling layers. In this work, a novel feature pooling method, named as mixed pooling, is proposed to regularize CNNs, which replaces the deterministic pooling operations with a stochastic procedure by randomly using the conventional max pooling and average pooling methods. The advantage of the proposed mixed pooling method lies in its wonderful ability to address the over-fitting problem encountered by CNN generation. Experimental results on three benchmark image classification datasets demonstrate that the proposed mixed pooling method is superior to max pooling, average pooling and some other state-of-the-art works known in the literature. 
5. Abstract of Cited Paper (BIB005):  Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers—8× deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.

Literature Review:
For encoding pooling, the average pooling strategy can be represented by: There are many works using average pooling. For instance, Pinto et al. BIB001 constructed a biologically inspired object recognition system, i.e., a simple V1-like model, based on average pooling BIB002 . This model outperforms state-of-the-art object recognition systems on a standard natural image recognition test. However, many researchers have shown that, in most encoding pooling-based vision applications, average pooling usually was not the best pooling strategy. For example, Boureau et al. BIB003 provided a theoretical and empirical insight into the performance between max pooling and average pooling. They pointed out that max pooling outperformed almost always average pooling, especially dramatically when using a linear SVM. For convolutional pooling, average pooling can be denoted as: where R a,b is a pooling region, and R a,b is its edge length. He et al. BIB006 introduced a deep residual net, called ResNet, for large-scale image recognition. The ResNet ended with a global average pooling layer and a fully connected layer with softmax. The authors won first place in several tracks in ILSVRC & COCO 2015 competitions by using the ResNet. For convolutional pooling-based vision applications, the situation that average pooling is always not the best choice is similar. For example, Sainath et al. BIB004 explored four pooling strategies in frequency only for an LVCSR speech task, and concluded that either l p pooling or Stochastic pooling can address the issues of max pooling or average pooling. Yu et al. BIB005 invented a mixed pooling method to regularize CNN. They demonstrated that the mixed pooling method is superior to both max pooling and average pooling, since the latter may reduce largely the feature map if there are many zero elements. In 2016 TRECVID competition, the CMU Informedia team [112] adopted the average-pooling for video representation in the multimedia event detection task, the Ad-hoc video search task, and the surveillance event detection task. Besides, they also adopted the average-pooling for document representation in the video hyperlinking task. Their experiments indicated that the average-pooling is an ideal discriminative strategy for hybrid representations.