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Vision Backbone Enhancement via Multi-Stage Cross-Scale Attention

Authors :
Shang, Liang
Liu, Yanli
Lou, Zhengyang
Quan, Shuxue
Adluru, Nagesh
Guan, Bochen
Sethares, William A.
Publication Year :
2023

Abstract

Convolutional neural networks (CNNs) and vision transformers (ViTs) have achieved remarkable success in various vision tasks. However, many architectures do not consider interactions between feature maps from different stages and scales, which may limit their performance. In this work, we propose a simple add-on attention module to overcome these limitations via multi-stage and cross-scale interactions. Specifically, the proposed Multi-Stage Cross-Scale Attention (MSCSA) module takes feature maps from different stages to enable multi-stage interactions and achieves cross-scale interactions by computing self-attention at different scales based on the multi-stage feature maps. Our experiments on several downstream tasks show that MSCSA provides a significant performance boost with modest additional FLOPs and runtime.

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2308.05872
Document Type :
Working Paper