Multimodal relation extraction is an essential task for knowledge graph construction. In this paper, we take an in-depth empirical analysis that indicates the inaccurate information in the visual scene graph leads to poor modal alignment weights, further degrading performance. Moreover, the visual shuffle experiments illustrate that the current approaches may not take full advantage of visual information. Based on the above observation, we further propose a strong baseline with an implicit fine-grained multimodal alignment based on Transformer for multimodal relation extraction. Experimental results demonstrate the better performance of our method. Codes are available at https://github.com/zjunlp/DeepKE/tree/main/example/re/multimodal.
@article{arxiv.2211.07504,
title = {On Analyzing the Role of Image for Visual-enhanced Relation Extraction},
author = {Lei Li and Xiang Chen and Shuofei Qiao and Feiyu Xiong and Huajun Chen and Ningyu Zhang},
journal= {arXiv preprint arXiv:2211.07504},
year = {2022}
}