The Experts below are selected from a list of 116589 Experts worldwide ranked by ideXlab platform
Honghui Shi - One of the best experts on this subject based on the ideXlab platform.
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alleviating Semantic Level shift a semi supervised domain adaptation method for Semantic segmentation
Computer Vision and Pattern Recognition, 2020Co-Authors: Zhonghao Wang, Rogerio S Feris, Jinjun Xiong, Thomas S Huang, Yunchao Wei, Wenmei W Hwu, Honghui ShiAbstract:Utilizing synthetic data for Semantic segmentation can significantly relieve human efforts in labelling pixel-Level masks. A key challenge of this task is how to alleviate the data distribution discrepancy between the source and target domains, i.e. reducing domain shift. The common approach to this problem is to minimize the discrepancy between feature distributions from different domains through adversarial training. However, directly aligning the feature distribution globally cannot guarantee consistency from a local view (i.e. Semantic-Level). To tackle this issue, we propose a semi-supervised approach named Alleviating Semantic-Level Shift (ASS), which can promote the distribution consistency from both global and local views. We apply our ASS to two domain adaptation tasks, from GTA5 to Cityscapes and from Synthia to Cityscapes. Extensive experiments demonstrate that: (1) ASS can significantly outperform the current unsupervised state-of-the-arts by employing a small number of annotated samples from the target domain; (2) ASS can beat the oracle model trained on the whole target dataset by over 3 points by augmenting the synthetic source data with annotated samples from the target domain without suffering from the prevalent problem of overfitting to the source domain.
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CVPR Workshops - Alleviating Semantic-Level Shift: A Semi-Supervised Domain Adaptation Method for Semantic Segmentation
2020 IEEE CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2020Co-Authors: Zhonghao Wang, Rogerio S Feris, Jinjun Xiong, Thomas S Huang, Yunchao Wei, Wenmei W Hwu, Honghui ShiAbstract:Utilizing synthetic data for Semantic segmentation can significantly relieve human efforts in labelling pixel-Level masks. A key challenge of this task is how to alleviate the data distribution discrepancy between the source and target domains, i.e. reducing domain shift. The common approach to this problem is to minimize the discrepancy between feature distributions from different domains through adversarial training. However, directly aligning the feature distribution globally cannot guarantee consistency from a local view (i.e. Semantic-Level). To tackle this issue, we propose a semi-supervised approach named Alleviating Semantic-Level Shift (ASS), which can promote the distribution consistency from both global and local views. We apply our ASS to two domain adaptation tasks, from GTA5 to Cityscapes and from Synthia to Cityscapes. Extensive experiments demonstrate that: (1) ASS can significantly outperform the current unsupervised state-of-the-arts by employing a small number of annotated samples from the target domain; (2) ASS can beat the oracle model trained on the whole target dataset by over 3 points by augmenting the synthetic source data with annotated samples from the target domain without suffering from the prevalent problem of overfitting to the source domain.
Zhonghao Wang - One of the best experts on this subject based on the ideXlab platform.
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alleviating Semantic Level shift a semi supervised domain adaptation method for Semantic segmentation
Computer Vision and Pattern Recognition, 2020Co-Authors: Zhonghao Wang, Rogerio S Feris, Jinjun Xiong, Thomas S Huang, Yunchao Wei, Wenmei W Hwu, Honghui ShiAbstract:Utilizing synthetic data for Semantic segmentation can significantly relieve human efforts in labelling pixel-Level masks. A key challenge of this task is how to alleviate the data distribution discrepancy between the source and target domains, i.e. reducing domain shift. The common approach to this problem is to minimize the discrepancy between feature distributions from different domains through adversarial training. However, directly aligning the feature distribution globally cannot guarantee consistency from a local view (i.e. Semantic-Level). To tackle this issue, we propose a semi-supervised approach named Alleviating Semantic-Level Shift (ASS), which can promote the distribution consistency from both global and local views. We apply our ASS to two domain adaptation tasks, from GTA5 to Cityscapes and from Synthia to Cityscapes. Extensive experiments demonstrate that: (1) ASS can significantly outperform the current unsupervised state-of-the-arts by employing a small number of annotated samples from the target domain; (2) ASS can beat the oracle model trained on the whole target dataset by over 3 points by augmenting the synthetic source data with annotated samples from the target domain without suffering from the prevalent problem of overfitting to the source domain.
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alleviating Semantic Level shift a semi supervised domain adaptation method for Semantic segmentation
arXiv: Computer Vision and Pattern Recognition, 2020Co-Authors: Zhonghao Wang, Rogerio S Feris, Jinjun Xiong, Thomas S HuangAbstract:Learning segmentation from synthetic data and adapting to real data can significantly relieve human efforts in labelling pixel-Level masks. A key challenge of this task is how to alleviate the data distribution discrepancy between the source and target domains, i.e. reducing domain shift. The common approach to this problem is to minimize the discrepancy between feature distributions from different domains through adversarial training. However, directly aligning the feature distribution globally cannot guarantee consistency from a local view (i.e. Semantic-Level), which prevents certain Semantic knowledge learned on the source domain from being applied to the target domain. To tackle this issue, we propose a semi-supervised approach named Alleviating Semantic-Level Shift (ASS), which can successfully promote the distribution consistency from both global and local views. Specifically, leveraging a small number of labeled data from the target domain, we directly extract Semantic-Level feature representations from both the source and the target domains by averaging the features corresponding to same categories advised by pixel-Level masks. We then feed the produced features to the discriminator to conduct Semantic-Level adversarial learning, which collaborates with the adversarial learning from the global view to better alleviate the domain shift. We apply our ASS to two domain adaptation tasks, from GTA5 to Cityscapes and from Synthia to Cityscapes. Extensive experiments demonstrate that: (1) ASS can significantly outperform the current unsupervised state-of-the-arts by employing a small number of annotated samples from the target domain; (2) ASS can beat the oracle model trained on the whole target dataset by over 3 points by augmenting the synthetic source data with annotated samples from the target domain without suffering from the prevalent problem of overfitting to the source domain.
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CVPR Workshops - Alleviating Semantic-Level Shift: A Semi-Supervised Domain Adaptation Method for Semantic Segmentation
2020 IEEE CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2020Co-Authors: Zhonghao Wang, Rogerio S Feris, Jinjun Xiong, Thomas S Huang, Yunchao Wei, Wenmei W Hwu, Honghui ShiAbstract:Utilizing synthetic data for Semantic segmentation can significantly relieve human efforts in labelling pixel-Level masks. A key challenge of this task is how to alleviate the data distribution discrepancy between the source and target domains, i.e. reducing domain shift. The common approach to this problem is to minimize the discrepancy between feature distributions from different domains through adversarial training. However, directly aligning the feature distribution globally cannot guarantee consistency from a local view (i.e. Semantic-Level). To tackle this issue, we propose a semi-supervised approach named Alleviating Semantic-Level Shift (ASS), which can promote the distribution consistency from both global and local views. We apply our ASS to two domain adaptation tasks, from GTA5 to Cityscapes and from Synthia to Cityscapes. Extensive experiments demonstrate that: (1) ASS can significantly outperform the current unsupervised state-of-the-arts by employing a small number of annotated samples from the target domain; (2) ASS can beat the oracle model trained on the whole target dataset by over 3 points by augmenting the synthetic source data with annotated samples from the target domain without suffering from the prevalent problem of overfitting to the source domain.
Thomas S Huang - One of the best experts on this subject based on the ideXlab platform.
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alleviating Semantic Level shift a semi supervised domain adaptation method for Semantic segmentation
Computer Vision and Pattern Recognition, 2020Co-Authors: Zhonghao Wang, Rogerio S Feris, Jinjun Xiong, Thomas S Huang, Yunchao Wei, Wenmei W Hwu, Honghui ShiAbstract:Utilizing synthetic data for Semantic segmentation can significantly relieve human efforts in labelling pixel-Level masks. A key challenge of this task is how to alleviate the data distribution discrepancy between the source and target domains, i.e. reducing domain shift. The common approach to this problem is to minimize the discrepancy between feature distributions from different domains through adversarial training. However, directly aligning the feature distribution globally cannot guarantee consistency from a local view (i.e. Semantic-Level). To tackle this issue, we propose a semi-supervised approach named Alleviating Semantic-Level Shift (ASS), which can promote the distribution consistency from both global and local views. We apply our ASS to two domain adaptation tasks, from GTA5 to Cityscapes and from Synthia to Cityscapes. Extensive experiments demonstrate that: (1) ASS can significantly outperform the current unsupervised state-of-the-arts by employing a small number of annotated samples from the target domain; (2) ASS can beat the oracle model trained on the whole target dataset by over 3 points by augmenting the synthetic source data with annotated samples from the target domain without suffering from the prevalent problem of overfitting to the source domain.
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alleviating Semantic Level shift a semi supervised domain adaptation method for Semantic segmentation
arXiv: Computer Vision and Pattern Recognition, 2020Co-Authors: Zhonghao Wang, Rogerio S Feris, Jinjun Xiong, Thomas S HuangAbstract:Learning segmentation from synthetic data and adapting to real data can significantly relieve human efforts in labelling pixel-Level masks. A key challenge of this task is how to alleviate the data distribution discrepancy between the source and target domains, i.e. reducing domain shift. The common approach to this problem is to minimize the discrepancy between feature distributions from different domains through adversarial training. However, directly aligning the feature distribution globally cannot guarantee consistency from a local view (i.e. Semantic-Level), which prevents certain Semantic knowledge learned on the source domain from being applied to the target domain. To tackle this issue, we propose a semi-supervised approach named Alleviating Semantic-Level Shift (ASS), which can successfully promote the distribution consistency from both global and local views. Specifically, leveraging a small number of labeled data from the target domain, we directly extract Semantic-Level feature representations from both the source and the target domains by averaging the features corresponding to same categories advised by pixel-Level masks. We then feed the produced features to the discriminator to conduct Semantic-Level adversarial learning, which collaborates with the adversarial learning from the global view to better alleviate the domain shift. We apply our ASS to two domain adaptation tasks, from GTA5 to Cityscapes and from Synthia to Cityscapes. Extensive experiments demonstrate that: (1) ASS can significantly outperform the current unsupervised state-of-the-arts by employing a small number of annotated samples from the target domain; (2) ASS can beat the oracle model trained on the whole target dataset by over 3 points by augmenting the synthetic source data with annotated samples from the target domain without suffering from the prevalent problem of overfitting to the source domain.
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CVPR Workshops - Alleviating Semantic-Level Shift: A Semi-Supervised Domain Adaptation Method for Semantic Segmentation
2020 IEEE CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2020Co-Authors: Zhonghao Wang, Rogerio S Feris, Jinjun Xiong, Thomas S Huang, Yunchao Wei, Wenmei W Hwu, Honghui ShiAbstract:Utilizing synthetic data for Semantic segmentation can significantly relieve human efforts in labelling pixel-Level masks. A key challenge of this task is how to alleviate the data distribution discrepancy between the source and target domains, i.e. reducing domain shift. The common approach to this problem is to minimize the discrepancy between feature distributions from different domains through adversarial training. However, directly aligning the feature distribution globally cannot guarantee consistency from a local view (i.e. Semantic-Level). To tackle this issue, we propose a semi-supervised approach named Alleviating Semantic-Level Shift (ASS), which can promote the distribution consistency from both global and local views. We apply our ASS to two domain adaptation tasks, from GTA5 to Cityscapes and from Synthia to Cityscapes. Extensive experiments demonstrate that: (1) ASS can significantly outperform the current unsupervised state-of-the-arts by employing a small number of annotated samples from the target domain; (2) ASS can beat the oracle model trained on the whole target dataset by over 3 points by augmenting the synthetic source data with annotated samples from the target domain without suffering from the prevalent problem of overfitting to the source domain.
Jinjun Xiong - One of the best experts on this subject based on the ideXlab platform.
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alleviating Semantic Level shift a semi supervised domain adaptation method for Semantic segmentation
Computer Vision and Pattern Recognition, 2020Co-Authors: Zhonghao Wang, Rogerio S Feris, Jinjun Xiong, Thomas S Huang, Yunchao Wei, Wenmei W Hwu, Honghui ShiAbstract:Utilizing synthetic data for Semantic segmentation can significantly relieve human efforts in labelling pixel-Level masks. A key challenge of this task is how to alleviate the data distribution discrepancy between the source and target domains, i.e. reducing domain shift. The common approach to this problem is to minimize the discrepancy between feature distributions from different domains through adversarial training. However, directly aligning the feature distribution globally cannot guarantee consistency from a local view (i.e. Semantic-Level). To tackle this issue, we propose a semi-supervised approach named Alleviating Semantic-Level Shift (ASS), which can promote the distribution consistency from both global and local views. We apply our ASS to two domain adaptation tasks, from GTA5 to Cityscapes and from Synthia to Cityscapes. Extensive experiments demonstrate that: (1) ASS can significantly outperform the current unsupervised state-of-the-arts by employing a small number of annotated samples from the target domain; (2) ASS can beat the oracle model trained on the whole target dataset by over 3 points by augmenting the synthetic source data with annotated samples from the target domain without suffering from the prevalent problem of overfitting to the source domain.
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alleviating Semantic Level shift a semi supervised domain adaptation method for Semantic segmentation
arXiv: Computer Vision and Pattern Recognition, 2020Co-Authors: Zhonghao Wang, Rogerio S Feris, Jinjun Xiong, Thomas S HuangAbstract:Learning segmentation from synthetic data and adapting to real data can significantly relieve human efforts in labelling pixel-Level masks. A key challenge of this task is how to alleviate the data distribution discrepancy between the source and target domains, i.e. reducing domain shift. The common approach to this problem is to minimize the discrepancy between feature distributions from different domains through adversarial training. However, directly aligning the feature distribution globally cannot guarantee consistency from a local view (i.e. Semantic-Level), which prevents certain Semantic knowledge learned on the source domain from being applied to the target domain. To tackle this issue, we propose a semi-supervised approach named Alleviating Semantic-Level Shift (ASS), which can successfully promote the distribution consistency from both global and local views. Specifically, leveraging a small number of labeled data from the target domain, we directly extract Semantic-Level feature representations from both the source and the target domains by averaging the features corresponding to same categories advised by pixel-Level masks. We then feed the produced features to the discriminator to conduct Semantic-Level adversarial learning, which collaborates with the adversarial learning from the global view to better alleviate the domain shift. We apply our ASS to two domain adaptation tasks, from GTA5 to Cityscapes and from Synthia to Cityscapes. Extensive experiments demonstrate that: (1) ASS can significantly outperform the current unsupervised state-of-the-arts by employing a small number of annotated samples from the target domain; (2) ASS can beat the oracle model trained on the whole target dataset by over 3 points by augmenting the synthetic source data with annotated samples from the target domain without suffering from the prevalent problem of overfitting to the source domain.
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CVPR Workshops - Alleviating Semantic-Level Shift: A Semi-Supervised Domain Adaptation Method for Semantic Segmentation
2020 IEEE CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2020Co-Authors: Zhonghao Wang, Rogerio S Feris, Jinjun Xiong, Thomas S Huang, Yunchao Wei, Wenmei W Hwu, Honghui ShiAbstract:Utilizing synthetic data for Semantic segmentation can significantly relieve human efforts in labelling pixel-Level masks. A key challenge of this task is how to alleviate the data distribution discrepancy between the source and target domains, i.e. reducing domain shift. The common approach to this problem is to minimize the discrepancy between feature distributions from different domains through adversarial training. However, directly aligning the feature distribution globally cannot guarantee consistency from a local view (i.e. Semantic-Level). To tackle this issue, we propose a semi-supervised approach named Alleviating Semantic-Level Shift (ASS), which can promote the distribution consistency from both global and local views. We apply our ASS to two domain adaptation tasks, from GTA5 to Cityscapes and from Synthia to Cityscapes. Extensive experiments demonstrate that: (1) ASS can significantly outperform the current unsupervised state-of-the-arts by employing a small number of annotated samples from the target domain; (2) ASS can beat the oracle model trained on the whole target dataset by over 3 points by augmenting the synthetic source data with annotated samples from the target domain without suffering from the prevalent problem of overfitting to the source domain.
Rogerio S Feris - One of the best experts on this subject based on the ideXlab platform.
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alleviating Semantic Level shift a semi supervised domain adaptation method for Semantic segmentation
Computer Vision and Pattern Recognition, 2020Co-Authors: Zhonghao Wang, Rogerio S Feris, Jinjun Xiong, Thomas S Huang, Yunchao Wei, Wenmei W Hwu, Honghui ShiAbstract:Utilizing synthetic data for Semantic segmentation can significantly relieve human efforts in labelling pixel-Level masks. A key challenge of this task is how to alleviate the data distribution discrepancy between the source and target domains, i.e. reducing domain shift. The common approach to this problem is to minimize the discrepancy between feature distributions from different domains through adversarial training. However, directly aligning the feature distribution globally cannot guarantee consistency from a local view (i.e. Semantic-Level). To tackle this issue, we propose a semi-supervised approach named Alleviating Semantic-Level Shift (ASS), which can promote the distribution consistency from both global and local views. We apply our ASS to two domain adaptation tasks, from GTA5 to Cityscapes and from Synthia to Cityscapes. Extensive experiments demonstrate that: (1) ASS can significantly outperform the current unsupervised state-of-the-arts by employing a small number of annotated samples from the target domain; (2) ASS can beat the oracle model trained on the whole target dataset by over 3 points by augmenting the synthetic source data with annotated samples from the target domain without suffering from the prevalent problem of overfitting to the source domain.
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alleviating Semantic Level shift a semi supervised domain adaptation method for Semantic segmentation
arXiv: Computer Vision and Pattern Recognition, 2020Co-Authors: Zhonghao Wang, Rogerio S Feris, Jinjun Xiong, Thomas S HuangAbstract:Learning segmentation from synthetic data and adapting to real data can significantly relieve human efforts in labelling pixel-Level masks. A key challenge of this task is how to alleviate the data distribution discrepancy between the source and target domains, i.e. reducing domain shift. The common approach to this problem is to minimize the discrepancy between feature distributions from different domains through adversarial training. However, directly aligning the feature distribution globally cannot guarantee consistency from a local view (i.e. Semantic-Level), which prevents certain Semantic knowledge learned on the source domain from being applied to the target domain. To tackle this issue, we propose a semi-supervised approach named Alleviating Semantic-Level Shift (ASS), which can successfully promote the distribution consistency from both global and local views. Specifically, leveraging a small number of labeled data from the target domain, we directly extract Semantic-Level feature representations from both the source and the target domains by averaging the features corresponding to same categories advised by pixel-Level masks. We then feed the produced features to the discriminator to conduct Semantic-Level adversarial learning, which collaborates with the adversarial learning from the global view to better alleviate the domain shift. We apply our ASS to two domain adaptation tasks, from GTA5 to Cityscapes and from Synthia to Cityscapes. Extensive experiments demonstrate that: (1) ASS can significantly outperform the current unsupervised state-of-the-arts by employing a small number of annotated samples from the target domain; (2) ASS can beat the oracle model trained on the whole target dataset by over 3 points by augmenting the synthetic source data with annotated samples from the target domain without suffering from the prevalent problem of overfitting to the source domain.
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CVPR Workshops - Alleviating Semantic-Level Shift: A Semi-Supervised Domain Adaptation Method for Semantic Segmentation
2020 IEEE CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2020Co-Authors: Zhonghao Wang, Rogerio S Feris, Jinjun Xiong, Thomas S Huang, Yunchao Wei, Wenmei W Hwu, Honghui ShiAbstract:Utilizing synthetic data for Semantic segmentation can significantly relieve human efforts in labelling pixel-Level masks. A key challenge of this task is how to alleviate the data distribution discrepancy between the source and target domains, i.e. reducing domain shift. The common approach to this problem is to minimize the discrepancy between feature distributions from different domains through adversarial training. However, directly aligning the feature distribution globally cannot guarantee consistency from a local view (i.e. Semantic-Level). To tackle this issue, we propose a semi-supervised approach named Alleviating Semantic-Level Shift (ASS), which can promote the distribution consistency from both global and local views. We apply our ASS to two domain adaptation tasks, from GTA5 to Cityscapes and from Synthia to Cityscapes. Extensive experiments demonstrate that: (1) ASS can significantly outperform the current unsupervised state-of-the-arts by employing a small number of annotated samples from the target domain; (2) ASS can beat the oracle model trained on the whole target dataset by over 3 points by augmenting the synthetic source data with annotated samples from the target domain without suffering from the prevalent problem of overfitting to the source domain.
