Vision Transformer with Deformable Attention

We propose a novel deformable self-attention module, where the positions of key and value pairs in self-attention are selected in a data-dependent way. This flexible scheme enables the self-attention module to focus on relevant regions and capture more informative features.

2022: Zhuofan Xia, Xuran Pan, Shiji Song, Li Erran Li, Gao Huang Ranked

#1 on Object Detection on COCO test-dev (AP metric)