Unveil: Unified Visual-Textual Integration and Distillation for Multi-modal Document Retrieval
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Computer Science > Computation and Language
Title:Unveil: Unified Visual-Textual Integration and Distillation for Multi-modal Document Retrieval
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)
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