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:  Multi-Scale Pooling 
1. Abstract of Cited Paper (BIB001):  Deep convolutional neural networks (CNN) have shown their promise as a universal representation for recognition. However, global CNN activations lack geometric invariance, which limits their robustness for classification and matching of highly variable scenes. To improve the invariance of CNN activations without degrading their discriminative power, this paper presents a simple but effective scheme called multi-scale orderless pooling (MOP-CNN). This scheme extracts CNN activations for local patches at multiple scale levels, performs orderless VLAD pooling of these activations at each level separately, and concatenates the result. The resulting MOP-CNN representation can be used as a generic feature for either supervised or unsupervised recognition tasks, from image classification to instance-level retrieval; it consistently outperforms global CNN activations without requiring any joint training of prediction layers for a particular target dataset. In absolute terms, it achieves state-of-the-art results on the challenging SUN397 and MIT Indoor Scenes classification datasets, and competitive results on ILSVRC2012/2013 classification and INRIA Holidays retrieval datasets. 
2. Abstract of Cited Paper (BIB002):  Existing deep convolutional neural networks (CNNs) require a fixed-size (e.g. 224×224) input image. This requirement is “artificial” and may hurt the recognition accuracy for the images or sub-images of an arbitrary size/scale. In this work, we equip the networks with a more principled pooling strategy, “spatial pyramid pooling”, to eliminate the above requirement. The new network structure, called SPP-net, can generate a fixed-length representation regardless of image size/scale. By removing the fixed-size limitation, we can improve all CNN-based image classification methods in general. Our SPP-net achieves state-of-the-art accuracy on the datasets of ImageNet 2012, Pascal VOC 2007, and Caltech101. 
3. Abstract of Cited Paper (BIB003):  We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. By a carefully crafted design, we increased the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation used in our submission for ILSVRC14 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection.

Literature Review:
For encoding pooling, Yang et al. BIB001 proposed a multi-scale spatial max pooling approach to generate nonlinear features based on sparse coding, as a generalized vector quantization, for fast yet accurate image classification. For convolutional pooling, Gong et al. BIB002 proposed a multi-scale orderless pooling approach to improve geometric invariance in CNN for classification and matching of highly variable scenes. He et al. BIB003 proposed a spatial pyramid pooling approach to generate fixed length representations for CNN-based visual recognition. The major contribution of the spatial pyramid pooling approach is its additional three-scale max-pooling layer. Szegedy et al. BIB004 proposed a 22-layer deep model, namely GoogLeNet, for classification and detection in the ImageNet challenge competition. The GoogLeNet employs a parallel multi-scale hybrid pooling architecture to reduce computing resources in deeper convolutions.