BiFormer: Vision Transformer with Bi-Level Routing Attention

As the core building block of vision transformers, attention is a powerful tool to capture long-range dependency. Empirical results across several computer vision tasks such as image classification, object detection, and semantic segmentation verify the effectiveness of our design.

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• 1 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.
• 2 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.
• 3 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.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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