Multi-Grained Query-Guided Set Prediction Network for Grounded Multimodal Named Entity Recognition

Grounded Multimodal Named Entity Recognition (GMNER) is an emerging information extraction (IE) task, aiming to simultaneously extract entity spans, types, and corresponding visual regions of entities from given sentence-image pairs data. Recent unified methods employing machine reading comprehension or sequence generation-based frameworks show limitations in this difficult task. The former, utilizing human-designed queries, struggles to differentiate ambiguous entities, such as Jordan (Person) and off-White x Jordan (Shoes). The latter, following the one-by-one decoding order, suffers from exposure bias issues. We maintain that these works misunderstand the relationships of multimodal entities. To tackle these, we propose a novel unified framework named Multi-grained Query-guided Set Prediction Network (MQSPN) to learn appropriate relationships at intra-entity and inter-entity levels. Specifically, MQSPN consists of a Multi-grained Query Set (MQS) and a Multimodal Set Prediction Network (MSP). MQS explicitly aligns entity regions with entity spans by employing a set of learnable queries to strengthen intra-entity connections. Based on distinct intra-entity modeling, MSP reformulates GMNER as a set prediction, guiding models to establish appropriate inter-entity relationships from a global matching perspective. Additionally, we incorporate a query-guided Fusion Net (QFNet) to work as a glue network between MQS and MSP. Extensive experiments demonstrate that our approach achieves state-of-the-art performances in widely used benchmarks.