Exploring High-Correlation Source Domain Information for Multi-Source Domain Adaptation in Semantic Segmentation

Author:

Cai Yuxiang1ORCID,Xi Meng1ORCID,Shang Yongheng1ORCID,Yin Jianwei1ORCID

Affiliation:

1. Zhejiang University, Hangzhou, China

Funder

Key Research and Development Program of Zhejiang Provence

National Natural Science Foundation of China

Publisher

ACM

Reference59 articles.

1. Self-supervised Augmentation Consistency for Adapting Semantic Segmentation

2. Vijay Badrinarayanan , Alex Kendall , and Roberto Cipolla . 2017 . Segnet: A deep convolutional encoder-decoder architecture for image segmentation . IEEE transactions on pattern analysis and machine intelligence, Vol. 39 , 12 (2017), 2481--2495. Vijay Badrinarayanan, Alex Kendall, and Roberto Cipolla. 2017. Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE transactions on pattern analysis and machine intelligence, Vol. 39, 12 (2017), 2481--2495.

3. Large-Scale Machine Learning with Stochastic Gradient Descent

4. Liang-Chieh Chen , George Papandreou , Iasonas Kokkinos , Kevin Murphy , and Alan L Yuille . 2017 . Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs . IEEE transactions on pattern analysis and machine intelligence, Vol. 40 , 4 (2017), 834--848. Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L Yuille. 2017. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE transactions on pattern analysis and machine intelligence, Vol. 40, 4 (2017), 834--848.

5. Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation

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