@inproceedings{liu-etal-2026-saliency,
title = "From Saliency to Discriminability: Rank-Preserving Visual Token Pruning for {VLM} Rerankers",
author = "Liu, Siyi and
Yang, Hanjun and
Zhang, Chenchen and
Zhu, Xiaorong and
Zuo, Xinyu and
Duan, Lisheng and
Liang, Haijin and
Ma, Jin and
Pu, Junfu and
Zhang, Yongqi",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1936/",
pages = "44052--44071",
ISBN = "979-8-89176-459-0",
abstract = "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 normalized attention entropy to decide when saliency can be trusted, fusing it with an attention-free rank discriminative prior and se-lecting pruning layers from the same trust landscape. Across three retrieval benchmarks and multiple VLM architectures, RaDiCal matches Dense MRR@10 on Flickr30K and surpasses it on MSCOCO at a 20{\%} token budget, ranks first among all pruning methods on FashionIQ, and holds within 1.2 pp on Flickr30K and MSCOCO at 10{\%} retention. It cuts FLOPs by 39{--}45{\%} and delivers 1.28{--}1.45{\texttimes} measured speedups across two VLM architectures without dataset-specific retuning."
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<abstract>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 normalized attention entropy to decide when saliency can be trusted, fusing it with an attention-free rank discriminative prior and se-lecting pruning layers from the same trust landscape. Across three retrieval benchmarks and multiple VLM architectures, RaDiCal matches Dense MRR@10 on Flickr30K and surpasses it on MSCOCO at a 20% token budget, ranks first among all pruning methods on FashionIQ, and holds within 1.2 pp on Flickr30K and MSCOCO at 10% retention. It cuts FLOPs by 39–45% and delivers 1.28–1.45× measured speedups across two VLM architectures without dataset-specific retuning.</abstract>
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%0 Conference Proceedings
%T From Saliency to Discriminability: Rank-Preserving Visual Token Pruning for VLM Rerankers
%A Liu, Siyi
%A Yang, Hanjun
%A Zhang, Chenchen
%A Zhu, Xiaorong
%A Zuo, Xinyu
%A Duan, Lisheng
%A Liang, Haijin
%A Ma, Jin
%A Pu, Junfu
%A Zhang, Yongqi
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F liu-etal-2026-saliency
%X 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 normalized attention entropy to decide when saliency can be trusted, fusing it with an attention-free rank discriminative prior and se-lecting pruning layers from the same trust landscape. Across three retrieval benchmarks and multiple VLM architectures, RaDiCal matches Dense MRR@10 on Flickr30K and surpasses it on MSCOCO at a 20% token budget, ranks first among all pruning methods on FashionIQ, and holds within 1.2 pp on Flickr30K and MSCOCO at 10% retention. It cuts FLOPs by 39–45% and delivers 1.28–1.45× measured speedups across two VLM architectures without dataset-specific retuning.
%U https://aclanthology.org/2026.emnlp-main.1936/
%P 44052-44071
Markdown (Informal)
[From Saliency to Discriminability: Rank-Preserving Visual Token Pruning for VLM Rerankers](https://aclanthology.org/2026.emnlp-main.1936/) (Liu et al., EMNLP 2026)
ACL
- Siyi Liu, Hanjun Yang, Chenchen Zhang, Xiaorong Zhu, Xinyu Zuo, Lisheng Duan, Haijin Liang, Jin Ma, Junfu Pu, and Yongqi Zhang. 2026. From Saliency to Discriminability: Rank-Preserving Visual Token Pruning for VLM Rerankers. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 44052–44071, Budapest, Hungary. Association for Computational Linguistics.