Boosting Lightweight CNN-Based Networks Via Selective Residual Attentive Patterns for Image Recognition
TL;DR
A simple fusion of two novel components of residual attentive information forms a robust volume of selective residual attentive patterns (named SRAP), which boosted the performance of lightweight CNN-based networks by up to ~7% on ImageNet-100 without increasing the computational complexity.
Abstract
Modern deep networks often rely on attention modules, which are still at a modest level due to using either one type of channel-wise pattern or an expensive combination of two types of them. In the case of using all of those, the obtained weights can be less discriminative due to the disjointed excitations, while the model complexity would double. To deal with these limitations, an efficient attention is proposed by addressing two novel components of residual attentive information as follows: 1) top- $n$ channel-residual attentive patterns with a unitary excitation perceptron, and 2) multiple spatial-residual attentive features. A simple fusion of these complementary components forms a robust volume of selective residual attentive patterns (named SRAP). Experiments on benchmark datasets for image classification have proved the prominent performance of SRAP versus other attention modules. Particularly, SRAP boosted the performance of lightweight CNN-based networks by up to ~7% on ImageNet-100 without increasing the computational complexity. The implementation code of SRAP is available at https://github.com/nttbdrk25/SRAP.