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Unreliability-Aware Disentangling for Cross-Domain Semi-supervised Pedestrian Detection

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Computer Vision – ACCV 2022 (ACCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13842))

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Abstract

The rapid progress of pedestrian detection is supported by the ever-growing labeled training data and elaborate neural-network-based model. However, adequate labeled training data are not always accessible when it comes to a new scene. Semi-supervised learning is promising for the case where a small amount of manually annotated images and a large amount of unannotated images are handy. In the semi-supervised setting, data generation is a powerful technique as a type of data augmentation. Some methods conduct data generation by disentangling pedestrian instances into different codes in latent space and combining codes of different instances to reconstruct new instances. However, these methods either work in a single domain or cannot handle the case where some instances are partially represented in the images. In this work, we propose to solve code-level information transferring from reliable domains to unreliable domains by incorporating a domain classifier that competes with the disentangling module to generate domain-invariant codes. An external classifier is trained on appearance-enhanced instances and sends integrity signals to the generative module, which facilitates the generative module to recognize fully/partially represented pedestrian instances. The resulting classifier ultimately renders high-quality pseudo-annotations for the unannotated data. The pseudo-annotated data, combined with a small amount of manually annotated data, are used to achieve a detector with more generalization and accuracy. We perform extensive experiments on multiple challenging benchmarks to demonstrate the effectiveness of the proposed method.

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Acknowledgements

This work was supported in part by the Natural Science Foundation of Guangdong Province (Project No. 2020A1515010484, 2022A1515011160), in part by the National Natural Science Foundation of China (Project No. 62072189), and in part by the Research Grants Council of the Hong Kong Special Administration Region (Project No. CityU 11201220).

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Authors and Affiliations

  1. Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong, China

    Wenhao Wu & Hau-San Wong

  2. School of Computer Science and Engineering, South China University of Technology, Guangzhou, China

    Si Wu

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  1. Wenhao Wu
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  2. Si Wu
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Correspondence to Si Wu .

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Editors and Affiliations

  1. University of Wollongong, Wollongong, NSW, Australia

    Lei Wang

  2. University of Bonn, Bonn, Germany

    Juergen Gall

  3. University of Adelaide, Adelaide, SA, Australia

    Tat-Jun Chin

  4. National Institute of Informatics, Tokyo, Japan

    Imari Sato

  5. Johns Hopkins University, Baltimore, MD, USA

    Rama Chellappa

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Wu, W., Wu, S., Wong, HS. (2023). Unreliability-Aware Disentangling for Cross-Domain Semi-supervised Pedestrian Detection. In: Wang, L., Gall, J., Chin, TJ., Sato, I., Chellappa, R. (eds) Computer Vision – ACCV 2022. ACCV 2022. Lecture Notes in Computer Science, vol 13842. Springer, Cham. https://doi.org/10.1007/978-3-031-26284-5_12

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  • DOI: https://doi.org/10.1007/978-3-031-26284-5_12

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