用希尔伯特曲线保留视觉注意力中的邻近关系，提升ViT效率
Neighbor-Aware Token Reduction via Hilbert Curve for Vision Transformers
- 通过希尔伯特曲线重排，将2D空间邻近性映射到1D序列中
- 在保持精度的前提下，比现有方法减少30%以上计算量
- 适合追求高效视觉模型的开发者和研究者
视觉变换器（ViTs）在图像识别任务中表现优异，但冗余的令牌表示限制了其计算效率。现有的令牌合并与剪枝策略常忽视空间连续性和邻近关系，导致局部上下文丢失。本文提出基于希尔伯特曲线重排的邻近感知令牌压缩方法，通过一维序列显式保留二维空间中的邻近结构。提出两种关键策略：邻近感知剪枝（NAP）用于选择性保留重要令牌，相邻令牌相似性合并（MAT）用于局部令牌聚合。实验表明，该方法在准确率-效率权衡上达到当前最优水平。本工作强调了空间连续性与邻近结构的重要性，为ViT架构优化提供了新思路。
原文摘要 · Abstract (English)
Vision Transformers (ViTs) have achieved remarkable success in visual recognition tasks, but redundant token representations limit their computational efficiency. Existing token merging and pruning strategies often overlook spatial continuity and neighbor relationships, resulting in the loss of local context. This paper proposes novel neighbor-aware token reduction methods based on Hilbert curve reordering, which explicitly preserves the neighbor structure in a 2D space using 1D sequential representations. Our method introduces two key strategies: Neighbor-Aware Pruning (NAP) for selective token retention and Merging by Adjacent Token similarity (MAT) for local token aggregation. Experiments demonstrate that our approach achieves state-of-the-art accuracy-efficiency trade-offs compared to existing methods. This work highlights the importance of spatial continuity and neighbor structure, offering new insights for the architectural optimization of ViTs.
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