Domain Adaptive Semantic Segmentation Without Source Data

Paper


You, Fuming, Li, Jingjing, Zhu, Lei, Chen, Zhi and Huang, Zi. 2021. "Domain Adaptive Semantic Segmentation Without Source Data." 29th ACM International Conference on Multimedia (MM '21). 20 - 24 Oct 2021 United States. Association for Computing Machinery (ACM). https://doi.org/10.1145/3474085.3475482
Paper/Presentation Title

Domain Adaptive Semantic Segmentation Without Source Data

Presentation TypePaper
AuthorsYou, Fuming, Li, Jingjing, Zhu, Lei, Chen, Zhi and Huang, Zi
Journal or Proceedings TitleProceedings of the 29th ACM International Conference on Multimedia (MM ’21)
Journal Citationpp. 3293-3302
Number of Pages10
Year2021
PublisherAssociation for Computing Machinery (ACM)
Place of PublicationUnited States
ISBN9781450386517
Digital Object Identifier (DOI)https://doi.org/10.1145/3474085.3475482
Web Address (URL) of Paperhttps://dl.acm.org/doi/abs/10.1145/3474085.3475482
Web Address (URL) of Conference Proceedingshttps://dl.acm.org/doi/proceedings/10.1145/3474085
Conference/Event29th ACM International Conference on Multimedia (MM '21)
Event Details
29th ACM International Conference on Multimedia (MM '21)
Parent
ACM International Conference on Multimedia
Delivery
Online
Event Date
20 to end of 24 Oct 2021
Abstract

Domain adaptive semantic segmentation is recognized as a promising technique to alleviate the domain shift between the labeled source domain and the unlabeled target domain in many real-world applications, such as automatic pilot. However, large amounts of source domain data often introduce significant costs in storage and training, and sometimes the source data is inaccessible due to privacy policies. To address these problems, we investigate domain adaptive semantic segmentation without source data, which assumes that the model is pre-trained on the source domain, and then adapting to the target domain without accessing source data anymore. Since there is no supervision from the source domain data, many self-training methods tend to fall into the winner-takes-all dilemma, where the majority classes totally dominate the segmentation networks and the networks fail to classify the minority classes. Consequently, we propose an effective framework for this challenging problem with two components: positive learning and negative learning. In positive learning, we select the class-balanced pseudo-labeled pixels with intra-class threshold, while in negative learning, for each pixel, we investigate which category the pixel does not belong to with the proposed heuristic complementary label selection. Notably, our framework can be easily implemented and incorporated with other methods to further enhance the performance. Extensive experiments on two widely-used synthetic-to-real benchmarks demonstrate our claims and the effectiveness of our framework, which outperforms the baseline with a large margin. Code is available at https://github.com/fumyou13/LDBE.

Keywordssource-free domain adaptation; transfer learnig; noisy label learning; self-training
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 20204602. Artificial intelligence
Public Notes

© 2021 Association for Computing Machinery. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in MM '21: Proceedings of the 29th ACM International Conference on Multimedia, https://doi.org/10.1145/3474085.3475482.

Byline AffiliationsUniversity of Electronic Science and Technology of China, China
Shandong Normal University, China
University of Queensland
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