From Saliency to Discriminability: Rank-Preserving Visual Token Pruning for VLM Rerankers
Large vision-language models used as listwise rerankers must jointly process visual tokens from tens of candidates per query, making token pruning essential for practical deployment. Existing pruning methods retain tokens by attention saliency, yet we show that saliency is systematically misaligned with ranking contribution: visually prominent tokens often capture order-neutral patterns shared across candidates. This mismatch is layer-dependent: saliency becomes informative only where attention is concentrated, and normalized attention entropy diagnoses the reliability shift (Pearson r=0.87). We propose RaDiCal (Rank-Discriminative Calibration), a training-free framework that uses normali...
| Authors | Siyi Liu, Hanjun Yang, Chenchen Zhang et al. |
|---|---|
| Primary category | cs.IR |
| Published | 1 September 2026 |
| Last updated at source | 1 September 2026 |
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