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Self-Attention has become prevalent in computer vision models. Inspired byfully connected Conditional Random Fields (CRFs), we decompose self-attentioninto local and context terms. They correspond to the unary and binary terms inCRF and are implemented by attention mechanisms with projection matrices. Weobserve that the unary terms only make small contributions to the outputs, andmeanwhile standard CNNs that rely solely on the unary terms achieve greatperformances on a variety of tasks. Therefore, we propose Locally EnhancedSelf-Attention (LESA), which enhances the unary term by incorporating it withconvolutions, and utilizes a fusion module to dynamically couple the unary andbinary operations. In our experiments, we replace the self-attention moduleswith LESA. The results on ImageNet and COCO show the superiority of LESA overconvolution and self-attention baselines for the tasks of image recognition,object detection, and instance segmentation. The code is made publiclyavailable.

Locally Enhanced Self-Attention: Combining Self-Attention and Convolution as Local and Context Terms