  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.
