Unveil: Unified Visual-Textual Integration and Distillation for Multi-modal Document Retrieval
arXiv:2605.24530v1 Announce Type: new 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 repr
The proliferation of diverse document formats and the increasing complexity of information retrieval demand more sophisticated methods that integrate visual and textual cues, which traditional approaches fail to address effectively.
Improved document retrieval through unified visual-textual integration will significantly enhance the efficiency and accuracy of information access across various enterprise and research domains.
The ability to accurately retrieve documents regardless of their format or visual layout moves beyond traditional text-only parsing, making more robust and context-sensitive search possible.
- · Enterprise AI
- · Information Management
- · Research Institutions
- · Cloud providers
- · Legacy document parsing software
- · Purely text-based search engines
More accurate and comprehensive information retrieval systems become widely adopted across industries.
This leads to faster decision-making processes and the discovery of previously obscure insights from complex document sets.
The enhanced capability for multimodal document understanding could fuel the development of more advanced AI agents that can interact with and process information in human-like ways.
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Read at arXiv cs.CL