Locality-Attending Vision Transformer
Vision transformers have demonstrated remarkable success in classification by leveraging global self-attention to capture long-range dependencies. However, this same mechanism can obscure fine-grained spatial details crucial for tasks such as segmentation. In this work, we seek to enhance segmentation performance of vision transformers after standard image-level classification training. More specifically, we present a simple yet effective add-on that improves performance on segmentation tasks while retaining vision transformers' image-level recognition capabilities. In our approach, we modulate the self-attention with a learnable Gaussian kernel that biases the attention toward neighborin...
| Authors | Sina Hajimiri, Farzad Beizaee, Fereshteh Shakeri et al. |
|---|---|
| Primary category | cs.CV |
| Published | 5 March 2026 |
| Last updated at source | 5 March 2026 |
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