@inproceedings{ma-etal-2025-towards-storage,
title = "Towards Storage-Efficient Visual Document Retrieval: An Empirical Study on Reducing Patch-Level Embeddings",
author = "Ma, Yubo and
Li, Jinsong and
Zang, Yuhang and
Wu, Xiaobao and
Dong, Xiaoyi and
Zhang, Pan and
Cao, Yuhang and
Duan, Haodong and
Wang, Jiaqi and
Cao, Yixin and
Sun, Aixin",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.1003/",
doi = "10.18653/v1/2025.findings-acl.1003",
pages = "19568--19580",
ISBN = "979-8-89176-256-5",
abstract = "Despite the strong performance of ColPali/ColQwen2 in Visualized Document Retrieval (VDR), its patch-level embedding approach leads to excessive memory usage. This empirical study investigates methods to reduce patch embeddings per page while minimizing performance degradation. We evaluate two token-reduction strategies: \textit{token pruning} and \textit{token merging}. Regarding token pruning, we surprisingly observe that a simple random strategy outperforms other sophisticated pruning methods, though still far from satisfactory. Further analysis reveals that pruning is inherently unsuitable for VDR as it requires removing certain page embeddings without query-specific information. Turning to token merging (more suitable for VDR), we search for the optimal combinations of merging strategy across three dimensions and develops Light-ColPali/ColQwen2. It maintains 98.2{\%} of retrieval performance with only 11.8{\%} of original memory usage, and preserves 94.6{\%} effectiveness at 2{\%} memory footprint. We expect our empirical findings and resulting Light-ColPali/ColQwen2 offer valuable insights and establish a competitive baseline for future efficient-VDR research."
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<abstract>Despite the strong performance of ColPali/ColQwen2 in Visualized Document Retrieval (VDR), its patch-level embedding approach leads to excessive memory usage. This empirical study investigates methods to reduce patch embeddings per page while minimizing performance degradation. We evaluate two token-reduction strategies: token pruning and token merging. Regarding token pruning, we surprisingly observe that a simple random strategy outperforms other sophisticated pruning methods, though still far from satisfactory. Further analysis reveals that pruning is inherently unsuitable for VDR as it requires removing certain page embeddings without query-specific information. Turning to token merging (more suitable for VDR), we search for the optimal combinations of merging strategy across three dimensions and develops Light-ColPali/ColQwen2. It maintains 98.2% of retrieval performance with only 11.8% of original memory usage, and preserves 94.6% effectiveness at 2% memory footprint. We expect our empirical findings and resulting Light-ColPali/ColQwen2 offer valuable insights and establish a competitive baseline for future efficient-VDR research.</abstract>
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%0 Conference Proceedings
%T Towards Storage-Efficient Visual Document Retrieval: An Empirical Study on Reducing Patch-Level Embeddings
%A Ma, Yubo
%A Li, Jinsong
%A Zang, Yuhang
%A Wu, Xiaobao
%A Dong, Xiaoyi
%A Zhang, Pan
%A Cao, Yuhang
%A Duan, Haodong
%A Wang, Jiaqi
%A Cao, Yixin
%A Sun, Aixin
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F ma-etal-2025-towards-storage
%X Despite the strong performance of ColPali/ColQwen2 in Visualized Document Retrieval (VDR), its patch-level embedding approach leads to excessive memory usage. This empirical study investigates methods to reduce patch embeddings per page while minimizing performance degradation. We evaluate two token-reduction strategies: token pruning and token merging. Regarding token pruning, we surprisingly observe that a simple random strategy outperforms other sophisticated pruning methods, though still far from satisfactory. Further analysis reveals that pruning is inherently unsuitable for VDR as it requires removing certain page embeddings without query-specific information. Turning to token merging (more suitable for VDR), we search for the optimal combinations of merging strategy across three dimensions and develops Light-ColPali/ColQwen2. It maintains 98.2% of retrieval performance with only 11.8% of original memory usage, and preserves 94.6% effectiveness at 2% memory footprint. We expect our empirical findings and resulting Light-ColPali/ColQwen2 offer valuable insights and establish a competitive baseline for future efficient-VDR research.
%R 10.18653/v1/2025.findings-acl.1003
%U https://aclanthology.org/2025.findings-acl.1003/
%U https://doi.org/10.18653/v1/2025.findings-acl.1003
%P 19568-19580
Markdown (Informal)
[Towards Storage-Efficient Visual Document Retrieval: An Empirical Study on Reducing Patch-Level Embeddings](https://aclanthology.org/2025.findings-acl.1003/) (Ma et al., Findings 2025)
ACL
- Yubo Ma, Jinsong Li, Yuhang Zang, Xiaobao Wu, Xiaoyi Dong, Pan Zhang, Yuhang Cao, Haodong Duan, Jiaqi Wang, Yixin Cao, and Aixin Sun. 2025. Towards Storage-Efficient Visual Document Retrieval: An Empirical Study on Reducing Patch-Level Embeddings. In Findings of the Association for Computational Linguistics: ACL 2025, pages 19568–19580, Vienna, Austria. Association for Computational Linguistics.