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arXiv · 2604.10167

Visual Late Chunking: An Empirical Study of Contextual Chunking for Efficient Visual Document Retrieval

Yibo Yan·Mingdong Ou·Yi Cao·Jiahao Huo·Xin Zou·Shuliang Liu·James Kwok·Xuming Hu
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Abstract

Multi-vector models dominate Visual Document Retrieval (VDR) due to their fine-grained matching capabilities, but their high storage and computational costs present a major barrier to practical deployment. In this paper, we propose ColChunk, a plug-and-play framework that introduces multimodal late chunking to construct efficient, contextualized multi-vectors. Unlike existing pruning or fixed-token approaches, ColChunk employs hierarchical clustering on patch-level embeddings, fused with a 2D position prior to ensure spatial-semantic coherence. This adaptive grouping allows for a content-aware representation that preserves global context while drastically reducing the vector count. Evaluations across 24 VDR datasets demonstrate ColChunk achieves over a 90% reduction in storage requirements while simultaneously delivering a 9-point average improvement in nDCG@5 across representative single-vector models. ColChunk provides a practical solution for balancing retrieval accuracy and efficiency in visual document systems.

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Yibo Yan, Mingdong Ou, Yi Cao, Jiahao Huo, Xin Zou, Shuliang Liu, James Kwok, Xuming Hu. 2026-04-11. Visual Late Chunking: An Empirical Study of Contextual Chunking for Efficient Visual Document Retrieval. https://arxiv.org/abs/2604.10167

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