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Recently, Transformers have shown promising performance in various visiontasks. To reduce the quadratic computation complexity caused by each queryattending to all keys/values, various methods have constrained the range ofattention within local regions, where each query only attends to keys/valueswithin a hand-crafted window. However, these hand-crafted window partitionmechanisms are data-agnostic and ignore their input content, so it is likelythat one query maybe attends to irrelevant keys/values. To address this issue,we propose a Dynamic Group Attention (DG-Attention), which dynamically dividesall queries into multiple groups and selects the most relevant keys/values foreach group. Our DG-Attention can flexibly model more relevant dependencieswithout any spatial constraint that is used in hand-crafted window basedattention. Built on the DG-Attention, we develop a general vision transformerbackbone named Dynamic Group Transformer (DGT). Extensive experiments show thatour models can outperform the state-of-the-art methods on multiple commonvision tasks, including image classification, semantic segmentation, objectdetection, and instance segmentation.

Dynamic Group Transformer: A General Vision Transformer Backbone with Dynamic Group Attention