March 26, 2021 - Conventional template matching ... In this paper, we propose an end-to-end Spatial Dual-Modality Graph Reasoning method (SDMG-R) to extract key information from unstructured document images....

March 26, 2021 - We model document images as ... The key information extraction is solved by iteratively propagating messages along graph edges and reasoning the categories of graph nodes....

March 26, 2021 - In this paper, we propose an end-to-end Spatial Dual-Modality Graph Reasoning method (SDMG-R) to extract key information from unstructured document images. We model document images as dual-modality graphs, nodes of which encode both the visual ...

In this paper, we propose an end-to-end Spatial Dual-Modality Graph Reasoning method (SDMG-R) to extract key information from unstructured document images. We model document images as dual-modality graphs, nodes of which encode both the visual ...

March 26, 2021 - In this paper, we propose an end-to-end Spatial Dual-Modality Graph Reasoning method (SDMG-R) to extract key information from unstructured document images. We model document images as dual-modality graphs, nodes of which encode both the visual ...

March 26, 2021 - We model document images as ... The key information extraction is solved by iteratively propagating messages along graph edges and reasoning the categories of graph nodes....

In this paper, we have proposed a novel spatial dual- modality graph reasoning model (termed SDMG-R) for key · information extraction from unstructured documents.

March 26, 2021 - We model document images as ... The key information extraction is solved by iteratively propagating messages along graph edges and reasoning the categories of graph nodes....

We model document images as ... neighboring text regions. The key information extraction is solved by iteratively propagating messages along graph edges and reasoning the categories of graph nodes....

March 26, 2021 - We model document images ... neighboring text regions . The key information extraction issolved by iteratively propagating messages along graph edges and reasoning thecategories of graph nodes ....

We model document images as ... The key information extraction is solved by iteratively propagating messages along graph edges and reasoning the categories of graph nodes....

We model document images as ... neighboring text regions. The key information extraction is solved by iteratively propagating messages along graph edges and reasoning the categories of graph nodes....

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Migrating from MMOCR 0.x · The implementation of the paper: Spatial Dual-Modality Graph Reasoning for Key Information Extraction. https://arxiv.org/abs/2103.14470

Paper tables with annotated results for Spatial Dual-Modality Graph Reasoning for Key Information Extraction

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March 3, 2023 - Specifically, we encode both the ... information extraction is solved by iteratively propagating text region messages along graph edges and reasoning the structured categories of graph nodes....

@misc{sun2021spatial, title={Spatial Dual-Modality Graph Reasoning for Key Information Extraction}, author={Hongbin Sun and Zhanghui Kuang and Xiaoyu Yue and Chenhao Lin and Wayne Zhang}, year={2021}, eprint={2103.14470}, archivePrefix={arXiv}, primaryClass={cs.CV} }

Multimodal Reasoning with Multimodal Knowledge Graph ... LLMs. In particular, a relation graph attention ... MMKGs. In particular, MR-MKG first encodes the re- trieved MMKG using a relation graph attention net- ... Wu et al. 2023c develop a KG-to-Text approach ... Figure 2: The overview of our MR-MKG approach. Text, multimodal knowledge graph, and image are independently · embedded and then concatenated to form prompt embedding tokens. A cross-modal alignment module is designed

1 month ago - Together, these findings position structured grounding as an effective medium for spatial reasoning and highlight adaptive modality selection as a key ingredient for reliable reasoning. Our framework depends on reliable intermediate spatial structure construction. Grid-based reasoning is helpful only when relation extraction and grid construction accurately reflect the original narrative; otherwise, extraction errors can propagate and reduce downstream accuracy.