  Vision Transformers (ViTs) have been shown to enhance visual recognition
through modeling long-range dependencies with multi-head self-attention (MHSA),
which is typically formulated as Query-Key-Value computation. However, the
attention map generated from the Query and Key captures only token-to-token
correlations at one single granularity. In this paper, we argue that
self-attention should have a more comprehensive mechanism to capture
correlations among tokens and groups (i.e., multiple adjacent tokens) for
higher representational capacity. Thereby, we propose Group-Mix Attention (GMA)
as an advanced replacement for traditional self-attention, which can
simultaneously capture token-to-token, token-to-group, and group-to-group
correlations with various group sizes. To this end, GMA splits the Query, Key,
and Value into segments uniformly and performs different group aggregations to
generate group proxies. The attention map is computed based on the mixtures of
tokens and group proxies and used to re-combine the tokens and groups in Value.
Based on GMA, we introduce a powerful backbone, namely GroupMixFormer, which
achieves state-of-the-art performance in image classification, object
detection, and semantic segmentation with fewer parameters than existing
models. For instance, GroupMixFormer-L (with 70.3M parameters and 384^2 input)
attains 86.2% Top-1 accuracy on ImageNet-1K without external data, while
GroupMixFormer-B (with 45.8M parameters) attains 51.2% mIoU on ADE20K.
