ReMeREC: Relation-aware and Multi-entity Referring Expression Comprehension

• URL: http://arxiv.org/abs/2507.16877v1
• Date: Tue, 22 Jul 2025 11:23:48 GMT
• Title: ReMeREC: Relation-aware and Multi-entity Referring Expression Comprehension
• Authors: Yizhi Hu, Zezhao Tian, Xingqun Qi, Chen Su, Bingkun Yang, Junhui Yin, Muyi Sun, Man Zhang, Zhenan Sun,
• Abstract summary: ReMeREC aims to localize specified entities or regions in an image based on natural language descriptions.<n>We first construct a relation-aware, multi-entity REC dataset called ReMeX.<n>We then propose ReMeREC, a novel framework that jointly leverages visual and textual cues to localize multiple entities.
• Score: 29.50623143244436
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Referring Expression Comprehension (REC) aims to localize specified entities or regions in an image based on natural language descriptions. While existing methods handle single-entity localization, they often ignore complex inter-entity relationships in multi-entity scenes, limiting their accuracy and reliability. Additionally, the lack of high-quality datasets with fine-grained, paired image-text-relation annotations hinders further progress. To address this challenge, we first construct a relation-aware, multi-entity REC dataset called ReMeX, which includes detailed relationship and textual annotations. We then propose ReMeREC, a novel framework that jointly leverages visual and textual cues to localize multiple entities while modeling their inter-relations. To address the semantic ambiguity caused by implicit entity boundaries in language, we introduce the Text-adaptive Multi-entity Perceptron (TMP), which dynamically infers both the quantity and span of entities from fine-grained textual cues, producing distinctive representations. Additionally, our Entity Inter-relationship Reasoner (EIR) enhances relational reasoning and global scene understanding. To further improve language comprehension for fine-grained prompts, we also construct a small-scale auxiliary dataset, EntityText, generated using large language models. Experiments on four benchmark datasets show that ReMeREC achieves state-of-the-art performance in multi-entity grounding and relation prediction, outperforming existing approaches by a large margin.

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