no code implementations • ICCV 2023 • Xinyue Huo, Lingxi Xie, Wengang Zhou, Houqiang Li, Qi Tian
Currently, a popular UDA framework lies in self-training which endows the model with two-fold abilities: (i) learning reliable semantics from the labeled images in the source domain, and (ii) adapting to the target domain via generating pseudo labels on the unlabeled images.
no code implementations • CVPR 2022 • Xinyue Huo, Lingxi Xie, Hengtong Hu, Wengang Zhou, Houqiang Li, Qi Tian
Unsupervised domain adaptation (UDA) is an important topic in the computer vision community.
no code implementations • CVPR 2021 • Xinyue Huo, Lingxi Xie, Jianzhong He, Zijie Yang, Wengang Zhou, Houqiang Li, Qi Tian
Semi-supervised learning is a useful tool for image segmentation, mainly due to its ability in extracting knowledge from unlabeled data to assist learning from labeled data.
no code implementations • 19 Nov 2020 • Xinyue Huo, Lingxi Xie, Longhui Wei, Xiaopeng Zhang, Hao Li, Zijie Yang, Wengang Zhou, Houqiang Li, Qi Tian
Contrastive learning has achieved great success in self-supervised visual representation learning, but existing approaches mostly ignored spatial information which is often crucial for visual representation.
no code implementations • 24 Jun 2020 • Xinyue Huo, Lingxi Xie, Jianzhong He, Zijie Yang, Qi Tian
This paper focuses on a popular pipeline known as self learning, and points out a weakness named lazy learning that refers to the difficulty for a model to learn from the pseudo labels generated by itself.