Article

Naive-Student: Leveraging Semi-Supervised Learning in Video Sequences for Urban Scene Segmentation

Authors:

Liang-Chieh Chen,

Raphael Gontijo Lopes,

Maxwell D. Collins,

Jonathon ShlensAuthors Info & Claims

Computer Vision – ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part IX

Pages 695 - 714

https://doi.org/10.1007/978-3-030-58545-7_40

Published: 23 August 2020 Publication History

Abstract

Supervised learning in large discriminative models is a mainstay for modern computer vision. Such an approach necessitates investing in large-scale human-annotated datasets for achieving state-of-the-art results. In turn, the efficacy of supervised learning may be limited by the size of the human annotated dataset. This limitation is particularly notable for image segmentation tasks, where the expense of human annotation is especially large, yet large amounts of unlabeled data may exist. In this work, we ask if we may leverage semi-supervised learning in unlabeled video sequences and extra images to improve the performance on urban scene segmentation, simultaneously tackling semantic, instance, and panoptic segmentation. The goal of this work is to avoid the construction of sophisticated, learned architectures specific to label propagation (e.g., patch matching and optical flow). Instead, we simply predict pseudo-labels for the unlabeled data and train subsequent models with both human-annotated and pseudo-labeled data. The procedure is iterated for several times. As a result, our Naive-Student model, trained with such simple yet effective iterative semi-supervised learning, attains state-of-the-art results at all three Cityscapes benchmarks, reaching the performance of 67.8% PQ, 42.6% AP, and 85.2% mIOU on the test set. We view this work as a notable step towards building a simple procedure to harness unlabeled video sequences and extra images to surpass state-of-the-art performance on core computer vision tasks.

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Published In

cover image Guide Proceedings

Computer Vision – ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part IX

Aug 2020

860 pages

ISBN:978-3-030-58544-0

DOI:10.1007/978-3-030-58545-7

Editors:
Andrea Vedaldi
University of Oxford, Oxford, UK
,
Horst Bischof
Graz University of Technology, Graz, Austria
,
Thomas Brox
University of Freiburg, Freiburg im Breisgau, Germany
,
Jan-Michael Frahm
University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

© Springer Nature Switzerland AG 2020.

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Published: 23 August 2020

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Cited By

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Yuan JGe JWang Zliu YEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)Semi-supervised Semantic Segmentation with Mutual Knowledge DistillationProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3611906(5436-5444)Online publication date: 26-Oct-2023
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Piao YLu CZhang MLu HKoyejo SMohamed SAgarwal ABelgrave DCho KOh A(2022)Semi-supervised video salient object detection based on uncertainty-guided pseudo labelsProceedings of the 36th International Conference on Neural Information Processing Systems10.5555/3600270.3600676(5614-5627)Online publication date: 28-Nov-2022
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