Multimodal Relation Extraction with Cross-Modal Retrieval and Synthesis

• URL: http://arxiv.org/abs/2305.16166v1
• Date: Thu, 25 May 2023 15:26:13 GMT
• Title: Multimodal Relation Extraction with Cross-Modal Retrieval and Synthesis
• Authors: Xuming Hu, Zhijiang Guo, Zhiyang Teng, Irwin King, Philip S. Yu
• Abstract summary: This research proposes to retrieve textual and visual evidence based on the object, sentence, and whole image. We develop a novel approach to synthesize the object-level, image-level, and sentence-level information for better reasoning between the same and different modalities.
• Score: 89.04041100520881
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal relation extraction (MRE) is the task of identifying the semantic relationships between two entities based on the context of the sentence image pair. Existing retrieval-augmented approaches mainly focused on modeling the retrieved textual knowledge, but this may not be able to accurately identify complex relations. To improve the prediction, this research proposes to retrieve textual and visual evidence based on the object, sentence, and whole image. We further develop a novel approach to synthesize the object-level, image-level, and sentence-level information for better reasoning between the same and different modalities. Extensive experiments and analyses show that the proposed method is able to effectively select and compare evidence across modalities and significantly outperforms state-of-the-art models.

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