Zero-Shot Human–Object Interaction Detection via Similarity Propagation

Human–object interaction (HOI) detection involves identifying interactions represented as $\langle \text {human, action, object} \rangle $ , requiring the localization of human–object pairs and interaction classification within an image. This work focuses on the challenge of detecting HOIs with unseen objects using the prevalent Transformer architecture. Our empirical analysis reveals that the performance degradation of novel HOI instances primarily arises from misclassifying unseen objects as confusable seen objects. To address this issue, we propose a similarity propagation (SP) scheme that leverages cosine similarity distance to regulate the prediction margin between seen and unseen objects. In addition, we introduce pseudo-supervision for unseen objects based on class semantic similarities during training. Furthermore, we incorporate semantic-aware instance-level and interaction-level contrastive losses with Transformer to enhance intraclass compactness and interclass separability, resulting in improved visual representations. Extensive experiments on two challenging benchmarks, V-COCO and HICO-DET, demonstrate the effectiveness of our model, outperforming current state-of-the-art methods under various zero-shot settings.