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ORCIDLihe Yang
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2020 – today
- 2024
[j1]Lihe Yang
, Wei Feng, Yaojun Wu, Liang Huang, Yinghui Quan:
Radar-Infrared Sensor Fusion Based on Hierarchical Features Mining. IEEE Signal Process. Lett. 31: 66-70 (2024)- [c8]Lihe Yang, Bingyi Kang, Zilong Huang, Xiaogang Xu, Jiashi Feng, Hengshuang Zhao:
Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data. CVPR 2024: 10371-10381 - [i11]Lihe Yang, Bingyi Kang, Zilong Huang, Xiaogang Xu, Jiashi Feng, Hengshuang Zhao:
Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data. CoRR abs/2401.10891 (2024) - [i10]Lihe Yang, Bingyi Kang, Zilong Huang, Zhen Zhao, Xiaogang Xu, Jiashi Feng, Hengshuang Zhao:
Depth Anything V2. CoRR abs/2406.09414 (2024) - [i9]Lihe Yang, Zhen Zhao, Hengshuang Zhao:
UniMatch V2: Pushing the Limit of Semi-Supervised Semantic Segmentation. CoRR abs/2410.10777 (2024) - 2023
- [c7]Lihe Yang, Lei Qi, Litong Feng, Wayne Zhang, Yinghuan Shi:
Revisiting Weak-to-Strong Consistency in Semi-Supervised Semantic Segmentation. CVPR 2023: 7236-7246 - [c6]Zhen Zhao, Lihe Yang, Sifan Long, Jimin Pi, Luping Zhou, Jingdong Wang:
Augmentation Matters: A Simple-Yet-Effective Approach to Semi-Supervised Semantic Segmentation. CVPR 2023: 11350-11359 - [c5]Yijiang Li, Xinjiang Wang, Lihe Yang, Litong Feng, Wayne Zhang, Ying Gao:
Diverse Cotraining Makes Strong Semi-Supervised Segmentor. ICCV 2023: 16009-16021 - [c4]Lihe Yang, Zhen Zhao, Lei Qi, Yu Qiao, Yinghuan Shi, Hengshuang Zhao:
Shrinking Class Space for Enhanced Certainty in Semi-Supervised Learning. ICCV 2023: 16141-16150 - [c3]Lihe Yang, Xiaogang Xu, Bingyi Kang, Yinghuan Shi, Hengshuang Zhao:
FreeMask: Synthetic Images with Dense Annotations Make Stronger Segmentation Models. NeurIPS 2023 - [i8]Lihe Yang, Zhen Zhao, Lei Qi, Yu Qiao, Yinghuan Shi, Hengshuang Zhao:
Shrinking Class Space for Enhanced Certainty in Semi-Supervised Learning. CoRR abs/2308.06777 (2023) - [i7]Yijiang Li, Xinjiang Wang, Lihe Yang, Litong Feng, Wayne Zhang, Ying Gao:
Diverse Cotraining Makes Strong Semi-Supervised Segmentor. CoRR abs/2308.09281 (2023) - [i6]Lihe Yang, Xiaogang Xu, Bingyi Kang, Yinghuan Shi, Hengshuang Zhao:
FreeMask: Synthetic Images with Dense Annotations Make Stronger Segmentation Models. CoRR abs/2310.15160 (2023) - [i5]Yau Shing Jonathan Cheung, Xi Chen, Lihe Yang, Hengshuang Zhao:
A Lightweight Clustering Framework for Unsupervised Semantic Segmentation. CoRR abs/2311.18628 (2023) - 2022
- [c2]Lihe Yang, Wei Zhuo, Lei Qi, Yinghuan Shi, Yang Gao:
ST++: Make Self-trainingWork Better for Semi-supervised Semantic Segmentation. CVPR 2022: 4258-4267 - [i4]Lihe Yang, Lei Qi, Litong Feng, Wayne Zhang, Yinghuan Shi:
Revisiting Weak-to-Strong Consistency in Semi-Supervised Semantic Segmentation. CoRR abs/2208.09910 (2022) - [i3]Zhen Zhao, Lihe Yang, Sifan Long, Jimin Pi, Luping Zhou, Jingdong Wang:
Augmentation Matters: A Simple-yet-Effective Approach to Semi-supervised Semantic Segmentation. CoRR abs/2212.04976 (2022) - 2021
- [c1]Lihe Yang, Wei Zhuo, Lei Qi, Yinghuan Shi, Yang Gao:
Mining Latent Classes for Few-shot Segmentation. ICCV 2021: 8701-8710 - [i2]Lihe Yang, Wei Zhuo, Lei Qi, Yinghuan Shi, Yang Gao:
Mining Latent Classes for Few-shot Segmentation. CoRR abs/2103.15402 (2021) - [i1]Lihe Yang, Wei Zhuo, Lei Qi, Yinghuan Shi, Yang Gao:
ST++: Make Self-training Work Better for Semi-supervised Semantic Segmentation. CoRR abs/2106.05095 (2021)
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