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

Referring Expression Comprehension (REC) aims to localize specified entities or regions from the source image according to the given natural language descriptions. While existing methods enable single-entity localization, they overlook modeling the complex inter-entity relationship in more practical multi-entity scenes, which limits their ability to produce accurate and reliable results. Moreover, the lack of high-quality multi-entity datasets incorporating fine-grained and paired image-text-relation annotations also limits addressing this challenge. To achieve this task, we first manually construct a relation-aware multi-entity REC dataset with fine-grained relation and text annotations, namely ReMeX. Additionally, we propose ReMeREC, a novel framework that effectively integrates textual and visual cues to localize multiple entities while capturing their inter-relationship. Specifically, to mitigate the semantic ambiguity arising from the absence of explicit entity boundaries in the source natural language description, we introduce a novel Text-adaptive Multi-entity Perceptron (TMP). TMP dynamically infers both the quantity and span of entities from corresponding fine-grained text cues, thus deriving representations that preserve the unique characteristics of each entity. Meanwhile, we design the Entity Inter-relationship Reasoner (EIR) to enhance semantic distinctiveness relationship modeling, leading to a more profound perception of the global scene. Furthermore, to better capture the fine-grained linguistic prompts for delineating multiple entity boundaries and inter-relationship, we leverage LLMs to generate a small-scale textual dataset, dubbed EntityText, which serves as an effective auxiliary resource and further improves the textual understanding. Extensive experiments conducted on four benchmark datasets demonstrate the superior performance of our framework. Remarkably, ReMeREC achieves outstanding results in multi-entity grounding and complex relationship prediction, outperforming other counterparts by a large margin.