@inproceedings{liu-etal-2026-hiprune,
title = "{H}i{P}rune: Hierarchical Attention for Efficient Token Pruning in Vision-Language Models",
author = "Liu, Jizhihui and
Zhu, Guangdao and
Du, Feiyi and
Lian, Niu and
Li, Jun and
Chen, Bin and
Guan, Weili and
Wang, Yaowei",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.162/",
pages = "3274--3291",
ISBN = "979-8-89176-395-1",
abstract = "Vision-Language Models (VLMs) encode images and videos into abundant tokens, which contain substantial redundancy and computation cost. While visual token pruning mitigates the issue, most existing methods lack insight into the intrinsic property of the vision encoder itself. In this work, we dive into the vision encoder and prove that the middle layers pay more attention to the main objects of the image qualitatively and quantitatively, while the deep layers to tokens with rich global information. Utilizing this Hierarchical attention pattern, we propose HiPrune, a training-free and model-agnostic token Pruning method. HiPrune identifies three types of visual tokens according to their attention in different phases of the vision encoder, which preserves different levels of information. By coupling with the similarity of text tokens, we propose a prompt-aware variance, HiPrune++, which further improves instruction following performance under a very low token budget. Extensive experiments across four representative VLMs show that HiPrune achieves up to 99.3{\%} of task accuracy with only 1/3 of the tokens, while reducing inference FLOPs by 58.7{\%}. HiPrune++ maintains up to 99.9{\%} accuracy with 2/9 tokens, highlighting robustness under high-resolution."
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<abstract>Vision-Language Models (VLMs) encode images and videos into abundant tokens, which contain substantial redundancy and computation cost. While visual token pruning mitigates the issue, most existing methods lack insight into the intrinsic property of the vision encoder itself. In this work, we dive into the vision encoder and prove that the middle layers pay more attention to the main objects of the image qualitatively and quantitatively, while the deep layers to tokens with rich global information. Utilizing this Hierarchical attention pattern, we propose HiPrune, a training-free and model-agnostic token Pruning method. HiPrune identifies three types of visual tokens according to their attention in different phases of the vision encoder, which preserves different levels of information. By coupling with the similarity of text tokens, we propose a prompt-aware variance, HiPrune++, which further improves instruction following performance under a very low token budget. Extensive experiments across four representative VLMs show that HiPrune achieves up to 99.3% of task accuracy with only 1/3 of the tokens, while reducing inference FLOPs by 58.7%. HiPrune++ maintains up to 99.9% accuracy with 2/9 tokens, highlighting robustness under high-resolution.</abstract>
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%0 Conference Proceedings
%T HiPrune: Hierarchical Attention for Efficient Token Pruning in Vision-Language Models
%A Liu, Jizhihui
%A Zhu, Guangdao
%A Du, Feiyi
%A Lian, Niu
%A Li, Jun
%A Chen, Bin
%A Guan, Weili
%A Wang, Yaowei
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F liu-etal-2026-hiprune
%X Vision-Language Models (VLMs) encode images and videos into abundant tokens, which contain substantial redundancy and computation cost. While visual token pruning mitigates the issue, most existing methods lack insight into the intrinsic property of the vision encoder itself. In this work, we dive into the vision encoder and prove that the middle layers pay more attention to the main objects of the image qualitatively and quantitatively, while the deep layers to tokens with rich global information. Utilizing this Hierarchical attention pattern, we propose HiPrune, a training-free and model-agnostic token Pruning method. HiPrune identifies three types of visual tokens according to their attention in different phases of the vision encoder, which preserves different levels of information. By coupling with the similarity of text tokens, we propose a prompt-aware variance, HiPrune++, which further improves instruction following performance under a very low token budget. Extensive experiments across four representative VLMs show that HiPrune achieves up to 99.3% of task accuracy with only 1/3 of the tokens, while reducing inference FLOPs by 58.7%. HiPrune++ maintains up to 99.9% accuracy with 2/9 tokens, highlighting robustness under high-resolution.
%U https://aclanthology.org/2026.findings-acl.162/
%P 3274-3291
Markdown (Informal)
[HiPrune: Hierarchical Attention for Efficient Token Pruning in Vision-Language Models](https://aclanthology.org/2026.findings-acl.162/) (Liu et al., Findings 2026)
ACL
- Jizhihui Liu, Guangdao Zhu, Feiyi Du, Niu Lian, Jun Li, Bin Chen, Weili Guan, and Yaowei Wang. 2026. HiPrune: Hierarchical Attention for Efficient Token Pruning in Vision-Language Models. In Findings of the Association for Computational Linguistics: ACL 2026, pages 3274–3291, San Diego, California, United States. Association for Computational Linguistics.