no code implementations • 4 Dec 2023 • Longhui Yuan, Shuang Li, Zhuo He, Binhui Xie
Extensive experiments demonstrate that ATASeg bridges the performance gap between TTA methods and their supervised counterparts with only extremely few annotations, even one click for labeling surpasses known SOTA TTA methods by 2. 6% average mIoU on ACDC benchmark.
1 code implementation • 7 Oct 2023 • Shuang Li, Longhui Yuan, Binhui Xie, Tao Yang
Test-time adaptation (TTA) adapts the pre-trained models to test distributions during the inference phase exclusively employing unlabeled test data streams, which holds great value for the deployment of models in real-world applications.
1 code implementation • CVPR 2023 • Longhui Yuan, Binhui Xie, Shuang Li
Test-time adaptation (TTA) intends to adapt the pretrained model to test distributions with only unlabeled test data streams.
1 code implementation • 2 Dec 2021 • Binhui Xie, Longhui Yuan, Shuang Li, Chi Harold Liu, Xinjing Cheng, Guoren Wang
Unsupervised domain adaptation has recently emerged as an effective paradigm for generalizing deep neural networks to new target domains.
1 code implementation • CVPR 2022 • Binhui Xie, Longhui Yuan, Shuang Li, Chi Harold Liu, Xinjing Cheng
Self-training has greatly facilitated domain adaptive semantic segmentation, which iteratively generates pseudo labels on unlabeled target data and retrains the network.