arXiv — NLP / Computation & Language · · 3 min read

Unveil: Unified Visual-Textual Integration and Distillation for Multi-modal Document Retrieval

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Computer Science > Computation and Language

arXiv:2605.24530 (cs)
[Submitted on 23 May 2026]

Title:Unveil: Unified Visual-Textual Integration and Distillation for Multi-modal Document Retrieval

View a PDF of the paper titled Unveil: Unified Visual-Textual Integration and Distillation for Multi-modal Document Retrieval, by Hao Sun and 6 other authors
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Abstract:Document retrieval in real-world scenarios faces significant challenges due to diverse document formats and modalities. Traditional text-based approaches rely on tailored parsing techniques that disregard layout information and are prone to errors, while recent parsing-free visual methods often struggle to capture fine-grained textual semantics in text-rich scenarios. To address these limitations, we propose \textbf{Unveil}, a novel visual-textual embedding framework that effectively integrates textual and visual features for robust document representation. Through knowledge distillation, we transfer the semantic understanding capabilities from the visual-textual embedding model to a purely visual model, enabling efficient parsing-free retrieval while preserving semantic fidelity. Experimental results demonstrate that our visual-textual embedding method surpasses existing approaches, while knowledge distillation successfully bridges the performance gap between visual-textual and visual-only methods, improving both retrieval accuracy and efficiency.
Comments: ACL 2025 Main Conference
Subjects: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2605.24530 [cs.CL]
  (or arXiv:2605.24530v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2605.24530
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Hao Sun [view email]
[v1] Sat, 23 May 2026 11:48:28 UTC (8,190 KB)
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