HAViT: Historical Attention Vision Transformer
Vision Transformers have excelled in computer vision but their attention mechanisms operate independently across layers, limiting information flow and feature learning. We propose an effective cross-layer attention propagation method that preserves and integrates historical attention matrices across encoder layers, offering a principled refinement of inter-layer information flow in Vision Transformers. This approach enables progressive refinement of attention patterns throughout the transformer hierarchy, enhancing feature acquisition and optimization dynamics. The method requires minimal architectural changes, adding only attention matrix storage and blending operations. Comprehensive ex...
| Authors | Swarnendu Banik, Manish Das, Shiv Ram Dubey et al. |
|---|---|
| Primary category | cs.CV |
| Published | 19 March 2026 |
| Last updated at source | 19 March 2026 |
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