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:  Max 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):  A common practice to gain invariant features in object recognition models is to aggregate multiple low-level features over a small neighborhood. However, the differences between those models makes a comparison of the properties of different aggregation functions hard. Our aim is to gain insight into different functions by directly comparing them on a fixed architecture for several common object recognition tasks. Empirical results show that a maximum pooling operation significantly outperforms subsampling operations. Despite their shift-invariant properties, overlapping pooling windows are no significant improvement over nonoverlapping pooling windows. By applying this knowledge, we achieve state-of-the-art error rates of 4.57% on the NORB normalized-uniform dataset and 5.6% on the NORB jittered-cluttered dataset. 
3. Abstract of Cited Paper (BIB003):  Convolutional Neural Networks (CNNs) are an alternative type of neural network that can be used to reduce spectral variations and model spectral correlations which exist in signals. Since speech signals exhibit both of these properties, CNNs are a more effective model for speech compared to Deep Neural Networks (DNNs). In this paper, we explore applying CNNs to large vocabulary speech tasks. First, we determine the appropriate architecture to make CNNs effective compared to DNNs for LVCSR tasks. Specifically, we focus on how many convolutional layers are needed, what is the optimal number of hidden units, what is the best pooling strategy, and the best input feature type for CNNs. We then explore the behavior of neural network features extracted from CNNs on a variety of LVCSR tasks, comparing CNNs to DNNs and GMMs. We find that CNNs offer between a 13-30% relative improvement over GMMs, and a 4-12% relative improvement over DNNs, on a 400-hr Broadcast News and 300-hr Switchboard task. 
4. Abstract of Cited Paper (BIB004):  Convolutional Neural Network (CNN) has demonstrated promising performance in single-label image classification tasks. However, how CNN best copes with multi-label images still remains an open problem, mainly due to the complex underlying object layouts and insufficient multi-label training images. In this work, we propose a flexible deep CNN infrastructure, called Hypotheses-CNN-Pooling (HCP), where an arbitrary number of object segment hypotheses are taken as the inputs, then a shared CNN is connected with each hypothesis, and finally the CNN output results from different hypotheses are aggregated with max pooling to produce the ultimate multi-label predictions. Some unique characteristics of this flexible deep CNN infrastructure include: 1) no ground-truth bounding box information is required for training; 2) the whole HCP infrastructure is robust to possibly noisy and/or redundant hypotheses; 3) the shared CNN is flexible and can be well pre-trained with a large-scale single-label image dataset, e.g., ImageNet; and 4) it may naturally output multi-label prediction results. Experimental results on Pascal VOC 2007 and VOC 2012 multi-label image datasets well demonstrate the superiority of the proposed HCP infrastructure over other state-of-the-arts. In particular, the mAP reaches 90.5% by HCP only and 93.2% after the fusion with our complementary result in [12] based on hand-crafted features on the VOC 2012 dataset.

Literature Review:
As one of the most welcome pooling strategies in vision related tasks, max pooling has been widely used in both encoding-based and convolutional-based pooling applications. For encoding pooling, the max pooling strategy has following formalization: pe k = max j∈{1,2··· ,m} e j,k . This max-pooling has been empirically justified by many algorithms. For example, Serre et al. BIB001 developed a biologically motivated framework. The framework adopts only two major kinds of computations, i.e., template matching and max pooling, to obtain a set of scale-and translation-invariant C2 features, for robust object recognition. Boureau et al. BIB002 conducted a theoretical analysis of average pooling and max pooling for visual recognition tasks. The authors concluded that the recognition performance using pooling strategies can be influenced by many factors, such as sample cardinality, resolution, codebook size, and max pooling performs no worse than average pooling in most cases. For convolutional pooling, the max pooling strategy can be formalized as: pc a,b = max where R a,b is a pooling region. There are also lots of researches using max pooling in CNN to generate deep features. For example, Sainath et al. BIB004 explored applying CNNs to large-vocabulary speech tasks, and showed that their convolutional network architecture, which consisted of a convolutional and max-pooling layer, was an improved CNN. Scherer et al. BIB003 evaluated two pooling operations in convolutional architectures for object recognition, and showed that a maximum pooling operation significantly outperformed a subsampling operation. Wei et al. BIB005 presented a flexible CNN framework, which can be pre-trained well on large-scale single-label image datasets, for multi-label image classification. The framework generated its ultimate multi-label predictions with a cross-hypothesis max-pooling operation on confidence vectors obtained from the input hypotheses using the shared CNN.