Search Results for author: Xinyue Huo

Found 5 papers, 0 papers with code

ATSO: Asynchronous Teacher-Student Optimization for Semi-Supervised Medical Image Segmentation

no code implementations24 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.

Autonomous Driving Image Segmentation +4

Heterogeneous Contrastive Learning: Encoding Spatial Information for Compact Visual Representations

no code implementations19 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.

Contrastive Learning Data Augmentation +1

ATSO: Asynchronous Teacher-Student Optimization for Semi-Supervised Image Segmentation

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.

Continual Learning Image Segmentation +3

Focus on Your Target: A Dual Teacher-Student Framework for Domain-adaptive Semantic Segmentation

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.

Semantic Segmentation Unsupervised Domain Adaptation

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