New attention methods aim to scale Vision Transformers efficiently

作者 PulseAugur 编辑部 · [2 个来源] · 2026-05-12 17:59

Two new research papers propose novel attention mechanisms for Vision Transformers (ViTs) to address the quadratic complexity issue with increasing image resolution. Representative Attention (RPAttention) uses learned representative tokens to mediate communication, allowing semantically related regions to interact regardless of spatial distance. Elastic Attention Cores (VECA) employs a core-periphery structure where patch tokens communicate exclusively through a small set of learned core embeddings, achieving linear complexity. AI

影响 These new attention mechanisms could enable Vision Transformers to scale more effectively to higher resolutions, potentially improving performance in tasks like object detection and segmentation.

排序理由 Two academic papers introduce new methods for improving Vision Transformer efficiency.

在 arXiv cs.CV 阅读 →

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报道来源 [2]

arXiv cs.CV TIER_1 English(EN) · Xiaojie Guo · 2026-05-14 14:48

Representative Attention For Vision Transformers

Linear attention has emerged as a promising direction for scaling Vision Transformers beyond the quadratic cost of dense self-attention. A prevalent strategy is to compress spatial tokens into a compact set of intermediate proxies that mediate global information exchange. However…
arXiv cs.CV TIER_1 English(EN) · Andrew F. Luo · 2026-05-12 17:59

Elastic Attention Cores for Scalable Vision Transformers

Vision Transformers (ViTs) achieve strong data-driven scaling by leveraging all-to-all self-attention. However, this flexibility incurs a computational cost that scales quadratically with image resolution, limiting ViTs in high-resolution domains. Underlying this approach is the …

报道来源 [2]

Representative Attention For Vision Transformers

Elastic Attention Cores for Scalable Vision Transformers

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