U-MERE: Unconstrained Multimodal Entity and Relation Extraction with Collaborative Modeling and Order-Sensitive Optimization

Existing multimodal entity and relation extraction tasks primarily focus on text-to-text or text-to-visual entity relations, overlooking real-world complexities involving visual-to-text and visual-to-visual cases, thus failing to capture the richer semantic structures in complex cross-modal interactions. To address the limitations, we propose a new task, Unconstrained Multimodal Entity and Relation Extraction (U-MERE), which jointly extracts arbitrary visual and textual entities, and their relations from image-text pairs. To accomplish U-MERE, we construct UMERE-Bench, a benchmark with over 9,000 samples that comprehensively covers four cross-modal entity relation directions and three task settings. Given the difficulty of jointly modeling diverse directions of cross-modal entity relations, we introduce Collaborative Modeling and Order-Sensitive (CMOS), which collaboratively guides large vision-language models (LVLMs) to decompose task complexity and mitigates generation order bias from fixed target relation sequences. CMOS employs small models to generate candidate entities, guiding LVLMs to capture key information and jointly optimizes multiple feasible relation orderings to reduce order dependency. Additionally, we design a Multimodal Order-aware Matching (MOM) evaluation method to align predictions with ground truth for precise assessment. Experimental results reveal that current LVLMs show limited performance on U-MERE, underscoring its inherent challenges, while CMOS consistently achieves superior performance across multiple advanced LVLMs, demonstrating its effectiveness and generalization capability. The dataset and code will be available in https://github.com/jiaweidoris/U-MERE.