# On Analyzing the Role of Image for Visual-enhanced Relation Extraction

arXiv: https://arxiv.org/abs/2211.07504 · HTML: https://mlsanity.com/paper/2211.07504/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 3 references that are in the map.
- First posted on arXiv 14 Nov 2022.

## Builds on (most cited first, 1 of 1)

- [Matching the Blanks: Distributional Similarity for Relation Learning](https://mlsanity.com/paper/1906.03158/) (2019)

## Built on by (most cited first, 5 of 5)

- [Information Screening whilst Exploiting! Multimodal Relation Extraction with Feature Denoising and Multimodal Topic Modeling](https://mlsanity.com/paper/2305.11719/) (2023)
- [Multimodal Relation Extraction with Cross-Modal Retrieval and Synthesis](https://mlsanity.com/paper/2305.16166/) (2023)
- [Prompt Me Up: Unleashing the Power of Alignments for Multimodal Entity and Relation Extraction](https://mlsanity.com/paper/2310.16822/) (2023)
- [Training on Synthetic Data Beats Real Data in Multimodal Relation Extraction](https://mlsanity.com/paper/2312.03025/) (2023)
- [REMOTE: A Unified Multimodal Relation Extraction Framework with Multilevel Optimal Transport and Mixture-of-Experts](https://mlsanity.com/paper/2509.04844/) (2025)

## Abstract (arXiv)

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.