  Human-Object Interaction Detection is a crucial aspect of human-centric scene
understanding, with important applications in various domains. Despite recent
progress in this field, recognizing subtle and detailed interactions remains
challenging. Existing methods try to use human-related clues to alleviate the
difficulty, but rely heavily on external annotations or knowledge, limiting
their practical applicability in real-world scenarios. In this work, we propose
a novel Part Semantic Network (PSN) to solve this problem. The core of PSN is a
Conditional Part Attention (CPA) mechanism, where human features are taken as
keys and values, and the object feature is used as query for the computation in
a cross-attention mechanism. In this way, our model learns to automatically
focus on the most informative human parts conditioned on the involved object,
generating more semantically meaningful features for interaction recognition.
Additionally, we propose an Occluded Part Extrapolation (OPE) strategy to
facilitate interaction recognition under occluded scenarios, which teaches the
model to extrapolate detailed features from partially occluded ones. Our method
consistently outperforms prior approaches on the V-COCO and HICO-DET datasets,
without external data or extra annotations. Additional ablation studies
validate the effectiveness of each component of our proposed method.
