Best Practices in Active Learning for Semantic Segmentation-Reference-Cited by-同舟云学术

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Best Practices in Active Learning for Semantic Segmentation

Published:2024 Issue: Volume: Page:427-442
ISSN:0302-9743
Container-title:Lecture Notes in Computer Science
language:en
Short-container-title:

Author:

Mittal Sudhanshu,Niemeijer Joshua,Schäfer Jörg P.,Brox Thomas

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-54605-1_28

Reference41 articles.

1. Brostow, G.J., Fauqueur, J., Cipolla, R.: Semantic object classes in video: a high-definition ground truth database. Pattern Recogn. Lett. 30, 88–97 (2008)

2. Cai, L., Xu, X., Liew, J.H., Foo, C.S.: Revisiting superpixels for active learning in semantic segmentation with realistic annotation costs. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2021)

3. Chapelle, O., Schölkopf, B., Zien, A. (eds.): Semi-Supervised Learning. The MIT Press, Cambridge (2006)

4. Chen, L., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. TPAMI 40(4), 834–848 (2018)

5. Chen, L., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. CoRR arxiv:1802.02611 (2018)

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1. ARSFineTune: On-the-Fly Tuning of Vision Models for Unmanned Ground Vehicles;2024 20th International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT);2024-04-29

2. Correlation-aware active learning for surgery video segmentation;2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV);2024-01-03

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