Papers › Vision Transformers with Patch Diversification
Vision Transformers with Patch Diversification
Chengyue Gong, Dilin Wang, Meng Li, Vikas Chandra, Qiang Liu
Vision transformer has demonstrated promising performance on challenging computer vision tasks. However, directly training the vision transformers may yield unstable and sub-optimal results. Recent works propose to improve the performance of the vision transformers by modifying the transformer structures, e.g., incorporating convolution layers. In contrast, we investigate an orthogonal approach to stabilize the vision transformer training without modifying the networks. We observe the instability of the training can be attributed to the significant similarity across the extracted patch representations. More specifically, for deep vision transformers, the self-attention blocks tend to map different patches into similar latent representations, yielding information loss and performance degradation. To alleviate this problem, in this work, we introduce novel loss functions in vision transformer training to explicitly encourage diversity across patch representations for more discriminative feature extraction. We empirically show that our proposed techniques stabilize the training and allow us to train wider and deeper vision transformers. We further show the diversified features significantly benefit the downstream tasks in transfer learning. For semantic segmentation, we enhance the state-of-the-art (SOTA) results on Cityscapes and ADE20k. Our code is available at https://github.com/ChengyueGongR/PatchVisionTransformer.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Semantic Segmentation | ADE20K | PatchDiverse + Swin-L (multi-scale test, upernet, ImageNet22k pretrain) | Validation mIoU | 54.4 | #61 of 235 | Archive leaderboard | report |
| Semantic Segmentation | ADE20K val | PatchDiverse + Swin-L (multi-scale test, upernet, ImageNet22k pretrain) | mIoU | 54.4% | #32 of 95 | Archive leaderboard | report |
| Semantic Segmentation | Cityscapes val | PatchDiverse + Swin-L (multi-scale test, upernet, ImageNet22k pretrain) | mIoU | 83.6% | #24 of 99 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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