arxivcs.CV2026-07-22
Not All Patches are Equal: Sampling Matters for Visible-Infrared Pre-Training
Qiwei Ma, Bin Deng, Junjie Zhu, Qiangjuan Huang, Puhong Duan, Ke Yang, et al.
Visible-infrared (VIS-IR) alignment is a key pre-training task for robust multi-sensor perception. Most existing methods use uniform patch-wise contrastive learning, but this can be unreliable in VIS-IR data because imaging-physics differences make some spatially paired regions i…