Enhanced Human–Object Interaction Detection via Maximum IoU Partitioning and Chunk Block Attention

Human-Object Interaction (HOI) detection presents substantial challenges, particularly in achieving precise spatial interpretation and managing computational efficiency. This paper introduces the Chunk Block Attention (CBA) mechanism, developed to address the limitations of traditional approaches that struggle to capture nuanced and overlapping interactions accurately. Leveraging Intersection over Union (IoU) metrics, CBA dynamically prioritizes high-relevance regions, enhancing focus on areas where human-object interactions are most likely to occur. Our experimental results, conducted on benchmark datasets such as HICO-DET and V-COCO, demonstrate significant improvements in both detection accuracy and interpretability over state-of-the-art methods. This advancement in computational focus not only increases accuracy in detecting complex human-object interactions but also optimizes resource usage, providing a robust solution for analyzing intricate interactions within dense visual environments. https://github.com/LHYUNMIN/qpic.