# Stochastic Region Pooling: Make Attention More Expressive

## TL;DR

This paper introduces Stochastic Region Pooling (SRP), a novel method to enhance channel-wise attention by increasing descriptor diversity without extra parameters, leading to improved image recognition performance.

## Contribution

SRP is a new, parameter-free pooling method that makes attention descriptors more representative and diverse, improving CNN performance across multiple datasets.

## Key findings

- SRP significantly improves accuracy on CIFAR-10/100 and ImageNet.
- SRP achieves state-of-the-art results on fine-grained datasets.
- SRP enhances attention mechanisms without additional computational cost.

## Abstract

Global Average Pooling (GAP) is used by default on the channel-wise attention mechanism to extract channel descriptors. However, the simple global aggregation method of GAP is easy to make the channel descriptors have homogeneity, which weakens the detail distinction between feature maps, thus affecting the performance of the attention mechanism. In this work, we propose a novel method for channel-wise attention network, called Stochastic Region Pooling (SRP), which makes the channel descriptors more representative and diversity by encouraging the feature map to have more or wider important feature responses. Also, SRP is the general method for the attention mechanisms without any additional parameters or computation. It can be widely applied to attention networks without modifying the network structure. Experimental results on image recognition datasets including CIAFR-10/100, ImageNet and three Fine-grained datasets (CUB-200-2011, Stanford Cars and Stanford Dogs) show that SRP brings the significant improvements of the performance over efficient CNNs and achieves the state-of-the-art results.