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Research Article | Open Access

Detecting human-object interaction with multi-level pairwise feature network

Key Laboratory of Pervasive Computing, Ministry of Education, BNRist, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
College of Information Sciences and Technology, Pennsylvania State University, University Park, PA 16802, USA
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Abstract

Human-object interaction (HOI) detection is crucial for human-centric image understanding which aims to infer ⟨human, action, object ⟩ triplets within an image. Recent studies often exploit visual features and the spatial configuration of a human-object pair in order to learn the action linking the human and object in the pair. We argue that such a paradigm of pairwise feature extraction and action inference can be applied not only at the whole human and object instance level, but also at the part level at which a body part interacts with an object, and at the semantic level by considering the semantic label of an object along with human appearance and human-object spatial configuration, to infer the action. We thus propose a multi-levelpairwise feature network (PFNet) for detecting human-object interactions. The network consists of threeparallel streams to characterize HOI utilizing pairwise features at the above three levels; the three streams are finally fused to give the action prediction. Extensive experiments show that our proposed PFNet outperforms other state-of-the-art methods on the V-COCO dataset and achieves comparable results to the state-of-the-art on the HICO-DET dataset.

Keywords

human-object interaction detectionpairwisefeature networkdeep learningmulti-level;object instance

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Computational Visual Media
Volume 7 Issue 2,
June 2021
Pages 229-239
DOI: 10.1007/s41095-020-0188-2
Cite this article:
Liu H, Mu T-J, Huang X. Detecting human-object interaction with multi-level pairwise feature network. Computational Visual Media, 2021, 7(2): 229-239. https://doi.org/10.1007/s41095-020-0188-2

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Received: 26 June 2020
Accepted: 20 July 2020
Published: 19 October 2020
© The Author(s) 2020

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