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Adversarial Semi-supervised Multi-domain Tracking

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Computer Vision – ACCV 2020 (ACCV 2020)

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Abstract

Neural networks for multi-domain learning empowers an effective combination of information from different domains by sharing and co-learning the parameters. In visual tracking, the emerging features in shared layers of a multi-domain tracker, trained on various sequences, are crucial for tracking in unseen videos. Yet, in a fully shared architecture, some of the emerging features are useful only in a specific domain, reducing the generalization of the learned feature representation. We propose a semi-supervised learning scheme to separate domain-invariant and domain-specific features using adversarial learning, to encourage mutual exclusion between them, and to leverage self-supervised learning for enhancing the shared features using the unlabeled reservoir. By employing these features and training dedicated layers for each sequence, we build a tracker that performs exceptionally on different types of videos.

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Authors and Affiliations

  1. RIKEN Center for Advanced Intelligence Project (AIP), Tokyo, Japan

    Kourosh Meshgi & Maryam Sadat Mirzaei

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  1. Kourosh Meshgi
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  2. Maryam Sadat Mirzaei
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Correspondence to Kourosh Meshgi .

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Editors and Affiliations

  1. Waseda University, Tokyo, Japan

    Hiroshi Ishikawa

  2. Institute of Automation of Chinese Academy of Sciences, Beijing, China

    Cheng-Lin Liu

  3. Czech Technical University in Prague, Prague, Czech Republic

    Tomas Pajdla

  4. University of Pennsylvania, Philadelphia, PA, USA

    Jianbo Shi

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Meshgi, K., Mirzaei, M.S. (2021). Adversarial Semi-supervised Multi-domain Tracking. In: Ishikawa, H., Liu, CL., Pajdla, T., Shi, J. (eds) Computer Vision – ACCV 2020. ACCV 2020. Lecture Notes in Computer Science(), vol 12623. Springer, Cham. https://doi.org/10.1007/978-3-030-69532-3_37

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