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Augmentation Matters: A Simple-yet-Effective Approach to Semi-supervised Semantic Segmentation
Zhao, Zhen, Yang, Lihe, Long, Sifan, Pi, Jimin, Zhou, Luping, Wang, Jingdong
Year of Publication 09.12.2022
Year of Publication 09.12.2022
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Journal Article
A Lightweight Clustering Framework for Unsupervised Semantic Segmentation
Yau Shing Jonathan Cheung, Chen, Xi, Yang, Lihe, Zhao, Hengshuang
Published in arXiv.org (30.11.2023)
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Published in arXiv.org (30.11.2023)
Paper
FreeMask: Synthetic Images with Dense Annotations Make Stronger Segmentation Models
Yang, Lihe, Xu, Xiaogang, Kang, Bingyi, Shi, Yinghuan, Zhao, Hengshuang
Published in arXiv.org (23.10.2023)
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Published in arXiv.org (23.10.2023)
Paper
Depth Anything V2
Yang, Lihe, Kang, Bingyi, Huang, Zilong, Zhao, Zhen, Xu, Xiaogang, Feng, Jiashi, Zhao, Hengshuang
Published in arXiv.org (13.06.2024)
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Published in arXiv.org (13.06.2024)
Paper
Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data
Yang, Lihe, Kang, Bingyi, Huang, Zilong, Xu, Xiaogang, Feng, Jiashi, Zhao, Hengshuang
Published in arXiv.org (07.04.2024)
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Published in arXiv.org (07.04.2024)
Paper
Diverse Cotraining Makes Strong Semi-Supervised Segmentor
Li, Yijiang, Wang, Xinjiang, Yang, Lihe, Feng, Litong, Zhang, Wayne, Gao, Ying
Published in arXiv.org (18.08.2023)
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Published in arXiv.org (18.08.2023)
Paper
Shrinking Class Space for Enhanced Certainty in Semi-Supervised Learning
Yang, Lihe, Zhao, Zhen, Qi, Lei, Yu, Qiao, Shi, Yinghuan, Zhao, Hengshuang
Published in arXiv.org (13.08.2023)
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Published in arXiv.org (13.08.2023)
Paper