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About this paper
Peng Zhang, Can Li, Liang Qiao, Zhanzhan Cheng, Shiliang Pu, Yi Niu, Fei Wu
The VSR framework unifies document layout analysis by combining visual, semantic, and relational information within a single adaptable system. This approach achieved F1 score improvements of 2-3% on the Article Regions dataset and consistent gains across PubLayNet and DocBank, demonstrating enhanced performance through adaptive multimodal feature fusion and explicit relation modeling.
Key insights
- Adaptive aggregation using attention mechanisms effectively combines visual and semantic features by weighing their relevance, outperforming simpler fusion strategies.
- Explicitly modeling relationships between document components through a Graph Neural Network consistently improves prediction accuracy, especially for ambiguous cases.
- A multi-granularity semantic stream, combining character and sentence-level embeddings, captures both fine-grained textual details and broader contextual information more effectively.
Summary by alphaXiv.
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