AI RESEARCH
Rank-Factorized Implicit Neural Bias: Scaling Super-Resolution Transformer with FlashAttention
arXiv CS.LG
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ArXi:2603.06738v1 Announce Type: new Recent Super-Resolution~(SR) methods mainly adopt Transformers for their strong long-range modeling capability and exceptional representational capacity. However, most SR Transformers rely heavily on relative positional bias~(RPB), which prevents them from leveraging hardware-efficient attention kernels such as FlashAttention. This limitation imposes a prohibitive computational burden during both