Progressive Unsupervised Learning for Visual Object Tracking
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
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Title of host publication | Proceedings - 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition |
Subtitle of host publication | CVPR 2021 |
Publisher | Institute of Electrical and Electronics Engineers, Inc. |
Pages | 2992-3001 |
ISBN (electronic) | 9781665445092 |
ISBN (print) | 9781665445108 |
Publication status | Published - 2021 |
Publication series
Name | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
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ISSN (Print) | 1063-6919 |
ISSN (electronic) | 2575-7075 |
Conference
Title | 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021) |
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Location | Virtual |
Period | 19 - 25 June 2021 |
Link(s)
Abstract
In this paper, we propose a progressive unsupervised learning (PUL) framework, which entirely removes the need for annotated training videos in visual tracking. Specifically, we first learn a background discrimination (BD) model that effectively distinguishes an object from background in a contrastive learning way. We then employ the BD model to progressively mine temporal corresponding patches (i.e., patches connected by a track) in sequential frames. As the BD model is imperfect and thus the mined patch pairs are noisy, we propose a noise-robust loss function to more effectively learn temporal correspondences from this noisy data. We use the proposed noise robust loss to train backbone networks of Siamese trackers. Without online fine-tuning or adaptation, our unsupervised real-time Siamese trackers can outperform state-of-the-art unsupervised deep trackers and achieve competitive results to the supervised baselines.
Citation Format(s)
Progressive Unsupervised Learning for Visual Object Tracking. / Wu, Qiangqiang; Wan, Jia; Chan, Antoni B.
Proceedings - 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition: CVPR 2021. Institute of Electrical and Electronics Engineers, Inc., 2021. p. 2992-3001 (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition).
Proceedings - 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition: CVPR 2021. Institute of Electrical and Electronics Engineers, Inc., 2021. p. 2992-3001 (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition).
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review