AI RESEARCH
HeSS: Head Sensitivity Score for Sparsity Redistribution in VGGT
arXiv CS.CV
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ArXi:2603.25336v1 Announce Type: new Visual Geometry Grounded Transformer (VGGT) has advanced 3D vision, yet its global attention layers suffer from quadratic computational costs that hinder scalability. Several sparsification-based acceleration techniques have been proposed to alleviate this issue, but they often suffer from substantial accuracy degradation. We hypothesize that the accuracy degradation stems from the heterogeneity in head-wise sparsification sensitivity, as the existing methods apply a uniform sparsity pattern across all heads.