AI RESEARCH
Advancing Vision Transformer with Enhanced Spatial Priors
arXiv CS.CV
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ArXi:2604.18549v1 Announce Type: new In recent years, the Vision Transformer (ViT) has garnered significant attention within the computer vision community. However, the core component of ViT, Self-Attention, lacks explicit spatial priors and suffers from quadratic computational complexity, limiting its applicability. To address these issues, we have proposed RMT, a robust vision backbone with explicit spatial priors for general purposes. RMT utilizes Manhattan distance decay to