Adversarial Semi-Supervised Multi-Domain Tracking

Kourosh Meshgi, Maryam Sadat Mirzaei; Proceedings of the Asian Conference on Computer Vision (ACCV), 2020


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 the target 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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@InProceedings{Meshgi_2020_ACCV, author = {Meshgi, Kourosh and Mirzaei, Maryam Sadat}, title = {Adversarial Semi-Supervised Multi-Domain Tracking}, booktitle = {Proceedings of the Asian Conference on Computer Vision (ACCV)}, month = {November}, year = {2020} }