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QLDT: adaptive Query Learning for HOI Detection via vision-language knowledge Transfer

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Abstract

Human-object interaction detection can be mainly categorized into two core problems, namely human-object association detection and interaction understanding. Firstly, for association detection, previous methods tend to directly detect obvious human-object interaction pairs, while ignoring some interaction pairs that may have potential interaction relationships, which is contrary to the actual situation. Secondly, for the interaction understanding problem, traditional methods face the challenges of long-tailed distribution and zero-shot detection, which cannot flexibly deal with complex and changing real-world scenarios. To this end, adaptive Query Learning for HOI Detection via vision-language knowledge Transfer(QLDT) is proposed. Specifically, a two-stage dynamic matching scoring algorithm based on dynamically changing thresholds and scores is designed to explore obscure H-O pairs and labeling to enlarge the sample size. Secondly, a visual-language pre-trained model GLIP (Grounded Language-Image Pre-training), is introduced to enhance the model’s interactive comprehension ability, extract the visual and linguistic features of the images through GLIP, minimize the gap with the predicted values using cross-entropy loss, and take the maximum value of the score with the obscure H-O pairs as the final prediction, which ensures the model’s positivity. The proposed method shows excellent performance on both HICO-DET and V-COCO datasets, for HICO-DET, QLDT achieved 35.37% mAP on the full category, 30.15% mAP on the rare category, and also improved on all five zero-shot metrics. For V-COCO, 62.74% mAP and 67.71% mAP were achieved under Scenario 1 and 2, respectively.

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Data Availability and Access

The data that support the findings of this study are openly available in HICO-DET and V-COCO.

Code Availability

The code of this paper is available at https://github.com/KKKarlW/QLDT.git

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Authors and Affiliations

  1. School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, 201620, Shanghai, China

    Xincheng Wang, Yongbin Gao, Wenjun Yu, Chenmou Wu, Mingxuan Chen & Honglei Ma

  2. COMAC Shanghai Aircraft Manufacturing Co., LTD, 201324, Shanghai, China

    Zhichao Chen

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  1. Xincheng Wang
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  2. Yongbin Gao
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  3. Wenjun Yu
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  4. Chenmou Wu
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  5. Mingxuan Chen
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  6. Honglei Ma
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  7. Zhichao Chen
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Contributions

Xincheng Wang performed the methodology and conceptualization; Yongbin Gao performed the review and supervision; Wenjun Yu performed the data curation; Chenmou Wu performed the validation; Mingxuan Chen performed the formal analysis; Honglei Ma performed the investigation; Zhichao Chen performed the data checks.

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Correspondence to Yongbin Gao.

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Informed consent was obtained from the Shanghai University of Engineering Science and COMAC Shanghai Aircraft Manufacturing Co., LTD for the publication of this article from all authors.

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The corresponding author of this paper is the associate editor of Applied Intelligence.

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Wang, X., Gao, Y., Yu, W. et al. QLDT: adaptive Query Learning for HOI Detection via vision-language knowledge Transfer. Appl Intell 54, 9008–9027 (2024). https://doi.org/10.1007/s10489-024-05653-1

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