Video-Level Multimodal Relation Extraction with Event-Entity Semantic Consistency

Previous research on Multimodal Relation Extraction (MRE) has primarily focused on identifying textual relations enhanced by static visual clues from images, benefiting fields such as multimedia analysis and knowledge graphs. With the rapid rise of video content on social media platforms, Multimodal Relation Extraction (MRE) systems face new challenges. To bridge this gap, we introduce Video-level Multimodal Relation Extraction (VMRE), a novel task aimed at extracting relational facts from videos. To advance this research, we present Vid-MRE, a new dataset containing 32 relation types and 12,402 multimodal relational facts, annotated across 3,970 pairs of textual news titles and corresponding videos. Since this task demands precise event and entity grounding to filter out excessive noise in the video, we propose an Event-Entity Semantic Consistency Network (E2SCN) to capture relational clues in the video effectively. Experimental results demonstrate that incorporating video content into the model significantly improves relation identification performance but also introduces more noise. Our E2SCN method effectively reduces the noise, enhancing fine-grained multimodal event and entity alignments while achieving state-of-the-art (SOTA) performance.