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Found 7 papers, 1 papers with code
HRVDA: High-Resolution Visual Document Assistant
In addition, we construct a document-oriented visual instruction tuning dataset and apply a multi-stage training strategy to enhance the model's document modeling capabilities.
Enhancing Visual Document Understanding with Contrastive Learning in Large Visual-Language Models
It can represent that the contrastive learning between the visual holistic representations and the multimodal fine-grained features of document objects can assist the vision encoder in acquiring more effective visual cues, thereby enhancing the comprehension of text-rich documents in LVLMs.
Attention Where It Matters: Rethinking Visual Document Understanding with Selective Region Concentration
We propose a novel end-to-end document understanding model called SeRum (SElective Region Understanding Model) for extracting meaningful information from document images, including document analysis, retrieval, and office automation.
Turning a CLIP Model into a Scene Text Spotter
Utilizing only 10% of the supervised data, FastTCM-CR50 improves performance by an average of 26. 5% and 5. 5% for text detection and spotting tasks, respectively.
ICDAR 2023 Competition on Structured Text Extraction from Visually-Rich Document Images
It is hoped that this competition will attract many researchers in the field of CV and NLP, and bring some new thoughts to the field of Document AI.
GMN: Generative Multi-modal Network for Practical Document Information Extraction
Document Information Extraction (DIE) has attracted increasing attention due to its various advanced applications in the real world.
Relational Representation Learning in Visually-Rich Documents
To deal with the unpredictable definition of relations, we propose a novel contrastive learning task named Relational Consistency Modeling (RCM), which harnesses the fact that existing relations should be consistent in differently augmented positive views.
