Human Object Interaction Detection
Human-object interaction (HOI) detection aims to identify and classify the actions occurring between humans and objects within images or videos, a crucial step towards comprehensive scene understanding. Current research heavily utilizes transformer-based models, often incorporating techniques like graph convolutional networks and attention mechanisms to effectively capture spatial relationships and contextual information, addressing challenges such as rare interactions and occlusion. Advances in HOI detection have significant implications for various applications, including robotics, video analysis, and assistive technologies, by enabling more nuanced and accurate interpretation of human activities within complex visual scenes.
Papers
Category-Aware Transformer Network for Better Human-Object Interaction Detection
Leizhen Dong, Zhimin Li, Kunlun Xu, Zhijun Zhang, Luxin Yan, Sheng Zhong, Xu Zou
Consistency Learning via Decoding Path Augmentation for Transformers in Human Object Interaction Detection
Jihwan Park, SeungJun Lee, Hwan Heo, Hyeong Kyu Choi, Hyunwoo J. Kim