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Pose attention and object semantic representation-based human-object interaction detection network

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Abstract

Human-object interaction (HOI) detection is a core problem in human-centric scene understanding, which is devoted to inferring triplets < human, verb, object > between humans and objects. Previous works mainly determine the interaction of each human-object pair by performing joint inference based on multiple features. In this paper, we design more discriminative representation of the human-object pair and a more effective HOI detection model. On the one hand, we use human poses as an attention mechanism to strengthen features, which is a novel way to deal with human poses in HOI detection. On the other hand, for a more effective representation of objects, a word vector is used to encode objects, and the relation features of humans and objects are captured by a graph convolution network based on object word vectors and human appearance features. These relation features are also strengthened by a human pose attention mechanism. Our model yields favorable results compared to the state-of-the-art HOI detection algorithms on two large-scale benchmark datasets, V-COCO and HICO-DET.

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Acknowledgements

The authors would like to thank the anonymous reviewers for their valuable and insightful comments on an earlier version of this manuscript.

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Authors and Affiliations

  1. School of Computer Science and Technology, Huaqiao University, Xiamen, 361000, China

    Wei-Mo Deng & Hong-Bo Zhang

  2. Xiamen Key Laboratory of Computer Vision and Pattern Recognition, Huaqiao University, Xiamen, 361000, China

    Qing Lei

  3. Fujian Key Laboratory of Big Data Intelligence and Security, Huaqiao University, Xiamen, 361000, China

    Ji-Xiang Du

  4. College of Humanities, Xiamen University, Xiamen, 361000, China

    Min Huang

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  1. Wei-Mo Deng
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  2. Hong-Bo Zhang
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  3. Qing Lei
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  5. Min Huang
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Correspondence to Hong-Bo Zhang.

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This work was supported by the Natural Science Foundation of China [No. 61871196, 62001176, 61902330 and 61673186]; National Key Research and Development Program of China [NO.2019YFC1604700]; Natural Science Foundation of Fujian Province of China [No. 2019J01082 and 2020J01085]; and the Promotion Program for Young and Middle-aged Teacher in Science and Technology Research of Huaqiao University [ZQN-YX601].

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Deng, WM., Zhang, HB., Lei, Q. et al. Pose attention and object semantic representation-based human-object interaction detection network. Multimed Tools Appl 81, 39453–39470 (2022). https://doi.org/10.1007/s11042-022-13146-x

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