Exploring the synergy between textual identity and visual signals in human-object interaction

Human-Object Interaction (HOI) detection task aims to recognize and understand interactions between humans and objects depicted in images. Unlike instance recognition tasks, which focus on isolated objects, HOI detection requires considering various explanatory factors, such as instance identity, spatial relationships, and scene context. However, previous HOI detection methods have primarily relied on local visual cues, often overlooking the vital role of instance identity and thus limiting the performance of models. In this paper, we introduce textual features to expand the definition of HOI representations, incorporating instance identity into the HOI reasoning process. Drawing inspiration from the human activity perception process, we explore the synergy between textual identity and visual signals to leverage various explanatory factors more effectively and enhance HOI detection performance. Specifically, our method extracts HOI explanatory factors using both modal representations. Visual features capture interactive cues, while textual features explicitly denote instance identities within human-object pairs, delineating relevant interaction categories. Additionally, we utilize Contrastive Language-Image Pre-training (CLIP) to enhance the semantic alignment between visual and textual features and design a cross-modal learning module for integrating HOI multimodal information. Extensive experiments on several benchmarks demonstrate that our proposed framework surpasses most existing methods, achieving outstanding performance with a mean average precision (mAP) of 33.95 on the HICO-DET dataset and 63.2 mAP on the V-COCO dataset.