TMHOI: Translational Model for Human-Object Interaction Detection

• URL: http://arxiv.org/abs/2303.04253v3
• Date: Sat, 1 Jul 2023 15:44:42 GMT
• Title: TMHOI: Translational Model for Human-Object Interaction Detection
• Authors: Lijing Zhu, Qizhen Lan, Alvaro Velasquez, Houbing Song, Acharya Kamal, Qing Tian, Shuteng Niu
• Abstract summary: We propose an innovative graph-based approach to detect human-object interactions (HOIs) Our method effectively captures the sentiment representation of HOIs by integrating both spatial and semantic knowledge. Our approach outperformed existing state-of-the-art graph-based methods by a significant margin.
• Score: 18.804647133922195
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Detecting human-object interactions (HOIs) is an intricate challenge in the field of computer vision. Existing methods for HOI detection heavily rely on appearance-based features, but these may not fully capture all the essential characteristics necessary for accurate detection. To overcome these challenges, we propose an innovative graph-based approach called TMGHOI (Translational Model for Human-Object Interaction Detection). Our method effectively captures the sentiment representation of HOIs by integrating both spatial and semantic knowledge. By representing HOIs as a graph, where the interaction components serve as nodes and their spatial relationships as edges. To extract crucial spatial and semantic information, TMGHOI employs separate spatial and semantic encoders. Subsequently, these encodings are combined to construct a knowledge graph that effectively captures the sentiment representation of HOIs. Additionally, the ability to incorporate prior knowledge enhances the understanding of interactions, further boosting detection accuracy. We conducted extensive evaluations on the widely-used HICO-DET datasets to demonstrate the effectiveness of TMGHOI. Our approach outperformed existing state-of-the-art graph-based methods by a significant margin, showcasing its potential as a superior solution for HOI detection. We are confident that TMGHOI has the potential to significantly improve the accuracy and efficiency of HOI detection. Its integration of spatial and semantic knowledge, along with its computational efficiency and practicality, makes it a valuable tool for researchers and practitioners in the computer vision community. As with any research, we acknowledge the importance of further exploration and evaluation on various datasets to establish the generalizability and robustness of our proposed method.

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