Record link: 
http://hdl.handle.net/20.500.12708/203932
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Title: 
Unsupervised Semantic Segmentation Through Depth-Guided Feature Correlation and Sampling
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Citation: 
Sick, L., Engel, D., Hermosilla, P., & Ropinski, T. (2024). Unsupervised Semantic Segmentation Through Depth-Guided Feature Correlation and Sampling. In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 3637–3646). https://doi.org/10.1109/CVPR52733.2024.00349
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Publisher DOI: 
10.1109/CVPR52733.2024.00349
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Publication Type: 
Inproceedings - Full-Paper Contribution
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Language: 
English
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Authors: 
Sick, Leon 
Engel, Dominik 
Hermosilla, Pedro 
Ropinski, Timo 
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Organisational Unit: 
E193-01 - Forschungsbereich Computer Vision 
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Published in: 
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 
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ISBN: 
979-8-3503-5300-6
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Date (published): 
2024
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Event name: 
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 
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Event date: 
16-Jun-2024 - 22-Jun-2024
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Event place: 
Seattle, WA, United States of America (the)
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Number of Pages: 
10
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Peer reviewed: 
Yes
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Keywords: 
Semantic Segmentation; Deep Learning; Unsupervised Learning; Depth Map
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Research Areas: 
Visual Computing and Human-Centered Technology: 100%
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Science Branch: 
1020 - Informatik: 90%
1010 - Mathematik: 10%
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Appears in Collections:
Conference Paper

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