Papers › DeBiFormer: Vision Transformer with Deformable Agent Bi-level Routing Attention
DeBiFormer: Vision Transformer with Deformable Agent Bi-level Routing Attention
Nguyen Huu Bao Long, Chenyu Zhang, Yuzhi Shi, Tsubasa Hirakawa, Takayoshi Yamashita, Tohgoroh Matsui, Hironobu Fujiyoshi
Vision Transformers with various attention modules have demonstrated superior performance on vision tasks. While using sparsity-adaptive attention, such as in DAT, has yielded strong results in image classification, the key-value pairs selected by deformable points lack semantic relevance when fine-tuning for semantic segmentation tasks. The query-aware sparsity attention in BiFormer seeks to focus each query on top-k routed regions. However, during attention calculation, the selected key-value pairs are influenced by too many irrelevant queries, reducing attention on the more important ones. To address these issues, we propose the Deformable Bi-level Routing Attention (DBRA) module, which optimizes the selection of key-value pairs using agent queries and enhances the interpretability of queries in attention maps. Based on this, we introduce the Deformable Bi-level Routing Attention Transformer (DeBiFormer), a novel general-purpose vision transformer built with the DBRA module. DeBiFormer has been validated on various computer vision tasks, including image classification, object detection, and semantic segmentation, providing strong evidence of its effectiveness.Code is available at {https://github.com/maclong01/DeBiFormer}
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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 |
|---|---|---|---|---|---|---|---|
| Image Classification | ImageNet | DeBiFormer-B | Top 1 Accuracy | 84.4% | #317 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | DeBiFormer-S | Top 1 Accuracy | 83.9% | #375 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | DeBiFormer-T | Top 1 Accuracy | 81.9% | #596 of 1060 | Archive leaderboard | report |
| Object Detection | COCO 2017 | DeBiFormer-B (IN1k pretrain, MaskRCNN 12ep) | mAP | 48.5 | #17 of 24 | Archive leaderboard | report |
| Object Detection | COCO 2017 | DeBiFormer-S (IN1k pretrain, MaskRCNN 12ep) | mAP | 47.5 | #19 of 24 | Archive leaderboard | report |
| Object Detection | COCO 2017 | DeBiFormer-B (IN1k pretrain, Retina) | mAP | 47.1 | #20 of 24 | Archive leaderboard | report |
| Object Detection | COCO 2017 | DeBiFormer-S (IN1k pretrain, Retina) | mAP | 45.6 | #21 of 24 | Archive leaderboard | report |
| Semantic Segmentation | ADE20K | DeBiFormer-B (IN1k pretrain, Upernet 160k) | Validation mIoU | 52.0 | #90 of 235 | 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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