Papers › BiFormer: Vision Transformer with Bi-Level Routing Attention

BiFormer: Vision Transformer with Bi-Level Routing Attention

15 Mar 2023CVPR 2023 1arXiv:2303.08810archive 2025-07-28

Lei Zhu, Xinjiang Wang, Zhanghan Ke, Wayne Zhang, Rynson Lau

As the core building block of vision transformers, attention is a powerful tool to capture long-range dependency. However, such power comes at a cost: it incurs a huge computation burden and heavy memory footprint as pairwise token interaction across all spatial locations is computed. A series of works attempt to alleviate this problem by introducing handcrafted and content-agnostic sparsity into attention, such as restricting the attention operation to be inside local windows, axial stripes, or dilated windows. In contrast to these approaches, we propose a novel dynamic sparse attention via bi-level routing to enable a more flexible allocation of computations with content awareness. Specifically, for a query, irrelevant key-value pairs are first filtered out at a coarse region level, and then fine-grained token-to-token attention is applied in the union of remaining candidate regions (\ie, routed regions). We provide a simple yet effective implementation of the proposed bi-level routing attention, which utilizes the sparsity to save both computation and memory while involving only GPU-friendly dense matrix multiplications. Built with the proposed bi-level routing attention, a new general vision transformer, named BiFormer, is then presented. As BiFormer attends to a small subset of relevant tokens in a \textbf{query adaptive} manner without distraction from other irrelevant ones, it enjoys both good performance and high computational efficiency, especially in dense prediction tasks. Empirical results across several computer vision tasks such as image classification, object detection, and semantic segmentation verify the effectiveness of our design. Code is available at \url{https://github.com/rayleizhu/BiFormer}.

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rayleizhu/biformer officialmentioned in papermentioned on GitHubpytorch report
chenller/mmseg-extension mentioned on GitHubpytorch report

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BiLevelRoutingAttention rayleizhu/biformer/ops/bra_legacy.py official repository ran · metamorphic tier: deterministic MIT (permissive) · bcfe9a38637858b7 · report
KVGather rayleizhu/biformer/ops/bra_legacy.py official repository ran MIT (permissive) · 0ce8acbb79bfdc96 · report
QKVLinear rayleizhu/biformer/ops/bra_legacy.py official repository ran · metamorphic tier: deterministic MIT (permissive) · d577326a684b4052 · report
TopkRouting rayleizhu/biformer/ops/bra_legacy.py official repository ran · metamorphic tier: deterministic fingerprinted MIT (permissive) · d272b520ceb75e5d · report
BiLevelRoutingAttention chenller/mmseg-extension/mmsegext/models/backbones/biformer.py community (archive-listed) ran · metamorphic tier: deterministic Apache-2.0 (permissive) · a90fa3e7967ab905 · report
TopkRouting chenller/mmseg-extension/mmsegext/models/backbones/biformer.py community (archive-listed) ran · metamorphic tier: deterministic fingerprinted Apache-2.0 (permissive) · 7f21ceacdd6fb9d6 · report

Tasks

Computational EfficiencyImage ClassificationObject DetectionSemantic Segmentationimage-classificationobject-detection

1 archive task tag without a task page not shown.

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Image Classification ImageNet BiFormer-B* (IN1k ptretrain) Top 1 Accuracy 85.4% #228 of 1060 Archive leaderboard report
Image Classification ImageNet BiFormer-S* (IN1k ptretrain) Top 1 Accuracy 84.3% #325 of 1060 Archive leaderboard report
Image Classification ImageNet BiFormer-T (IN1k ptretrain) Top 1 Accuracy 81.4% #644 of 1060 Archive leaderboard report
Object Detection COCO 2017 BiFormer-B (IN1k pretrain, MaskRCNN 12ep) mAP 48.6 #16 of 24 Archive leaderboard report
Object Detection COCO 2017 BiFormer-S (IN1k pretrain, MaskRCNN 12ep) mAP 47.8 #18 of 24 Archive leaderboard report
Semantic Segmentation ADE20K BiFormer-B (IN1k pretrain, Upernet 160k) Validation mIoU 51.7 #93 of 235 Archive leaderboard report
Semantic Segmentation ADE20K Upernet-BiFormer-S (IN1k pretrain, Upernet 160k) Validation mIoU 50.8 #110 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.

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