Exploring Pose-Aware Human-Object Interaction via Hybrid Learning

Human-Object Interaction (HOI) detection plays a crucial role in visual scene comprehension. In recent advancements two-stage detectors have taken a prominent position. However they are encumbered by two primary challenges. First the misalignment between feature representation and relation reasoning gives rise to a deficiency in discriminative features crucial for interaction detection. Second due to sparse annotation the second-stage interaction head generates numerous candidate <human object> pairs with only a small fraction receiving supervision. Towards these issues we propose a hybrid learning method based on pose-aware HOI feature refinement. Specifically we devise pose-aware feature refinement that encodes spatial features by considering human body pose characteristics. It can direct attention towards key regions ultimately offering a wealth of fine-grained features imperative for HOI detection. Further we introduce a hybrid learning method that combines HOI triplets with probabilistic soft labels supervision which is regenerated from decoupled verb-object pairs. This method explores the implicit connections between the interactions enhancing model generalization without requiring additional data. Our method establishes state-of-the-art performance on HICO-DET benchmark and excels notably in detecting rare HOIs.