EPSANet: An Efficient Pyramid Squeeze Attention Block on Convolutional Neural Network

TL;DR

EPSANet introduces a lightweight pyramid squeeze attention module that enhances CNN performance across multiple vision tasks, outperforming many existing attention methods with significant accuracy improvements.

Contribution

The paper proposes a novel, plug-and-play Pyramid Squeeze Attention (PSA) module and an efficient backbone architecture EPSANet that improve multi-scale feature representation in CNNs.

Findings

Outperforms state-of-the-art channel attention methods.

Improves ImageNet Top-1 accuracy by 1.93%.

Achieves +2.7 box AP in object detection and +1.7 mask AP in segmentation.

Abstract

Recently, it has been demonstrated that the performance of a deep convolutional neural network can be effectively improved by embedding an attention module into it. In this work, a novel lightweight and effective attention method named Pyramid Squeeze Attention (PSA) module is proposed. By replacing the 3x3 convolution with the PSA module in the bottleneck blocks of the ResNet, a novel representational block named Efficient Pyramid Squeeze Attention (EPSA) is obtained. The EPSA block can be easily added as a plug-and-play component into a well-established backbone network, and significant improvements on model performance can be achieved. Hence, a simple and efficient backbone architecture named EPSANet is developed in this work by stacking these ResNet-style EPSA blocks. Correspondingly, a stronger multi-scale representation ability can be offered by the proposed EPSANet for various computer vision tasks including but not limited to, image classification, object detection, instance segmentation, etc. Without bells and whistles, the performance of the proposed EPSANet outperforms most of the state-of-the-art channel attention methods. As compared to the SENet-50, the Top-1 accuracy is improved by 1.93% on ImageNet dataset, a larger margin of +2.7 box AP for object detection and an improvement of +1.7 mask AP for instance segmentation by using the Mask-RCNN on MS-COCO dataset are obtained. Our source code is available at:https://github.com/murufeng/EPSANet.