Making Vision Transformers Truly Shift-Equivariant

1University of Illinois at Urbana-Champaign, 2Purdue University.

Making Vision Transformers Truly Shift-Equivariant


For computer vision, Vision Transformers (ViTs) have become one of the go-to deep net architectures. Despite being inspired by Convolutional Neural Networks (CNNs), ViTs' output remains sensitive to small spatial shifts in the input, i.e., not shift invariant. To address this shortcoming, we introduce novel data-adaptive designs for each of the modules in ViTs, such as tokenization, self-attention, patch merging, and positional encoding. With our proposed modules, we achieve true shift-equivariance on four well-established ViTs, namely, Swin, SwinV2, CvT, and MViTv2. Empirically, we evaluate the proposed adaptive models on image classification and semantic segmentation tasks. These models achieve competitive performance across three different datasets while maintaining 100% shift consistency.


  title={Making Vision Transformers Truly Shift-Equivariant},
  author={Rojas-Gomez, Renan A and Lim, Teck-Yian and Do, Minh N and Yeh, Raymond A},
  journal={arXiv preprint arXiv:2305.16316},