Group Vision Transformer

The Vision Transformer has attained remarkable success in various computer vision applications. However, the large computational costs and complex design limit its ability in handling large feature maps. Existing research predominantly focuses on constraining attention to small local regions, which reduces the number of tokens attending the attention computation while overlooking computational demands caused by the feed-forward layer in the Vision Transformer block. In this paper, we introduce Group Vision Transformer (GVT), a relatively simple and efficient variant of Vision Transformer, aiming to improve attention computation. The core idea of our model is to divide and group the entire Transformer layer, instead of only the attention part, into multiple independent branches. This approach offers two advantages: (1) It helps reduce parameters and computational complexity; (2) it enhances the diversity of the learned features. We conduct comprehensive analysis of the impact of different numbers of groups on model performance, as well as their influence on parameters and computational complexity. Our proposed GVT demonstrates competitive performances in several common vision tasks. For example, our GVT-Tiny model achieves 84.8% top-1 accuracy on ImageNet-1K, 51.4% box mAP and 45.2% mask mAP on MS COCO object detection and instance segmentation, and 50.1% mIoU on ADE20K semantic segmentation, outperforming the CAFormer-S36 model by 0.3% in ImageNet-1K top-1 accuracy, 1.2% in box mAP, 1.0% in mask mAP on MS COCO object detection and instance segmentation, and 1.2% in mIoU on ADE20K semantic segmentation, with similar model parameters and computational complexity. Code is accessible at https://github.com/yaoppeng/GVT.