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Yandong Tang
Researcher at Chinese Academy of Sciences
Publications - 182
Citations - 2482
Yandong Tang is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Computer science & Image segmentation. The author has an hindex of 20, co-authored 151 publications receiving 1743 citations. Previous affiliations of Yandong Tang include Academia Sinica & Shenyang Institute of Automation.
Papers
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Journal ArticleDOI
RGBD Salient Object Detection via Deep Fusion
TL;DR: Zhang et al. as mentioned in this paper designed a new convolutional neural network (CNN) to automatically learn the interaction mechanism for RGBD salient object detection, which takes advantage of the knowledge obtained in traditional saliency detection by adopting various flexible and interpretable saliency feature vectors as inputs.
Proceedings ArticleDOI
DeshadowNet: A Multi-context Embedding Deep Network for Shadow Removal
TL;DR: An automatic and end-to-end deep neural network (DeshadowNet) to tackle shadow removal problems in a unified manner and shows that the proposed method performs favorably against several state-of-the-art methods.
Proceedings ArticleDOI
Video Desnowing and Deraining Based on Matrix Decomposition
TL;DR: This paper proposes a model based on matrix decomposition for video desnowing and deraining to solve the problems of snow/rain removal and shows that the proposed model performs better than the state-of-the-art methods for snow and rain removal.
Journal ArticleDOI
Methods and datasets on semantic segmentation: A review
TL;DR: Three categories of methods are reviewed and compared, including those based on hand-engineered features, learned features and weakly supervised learning, and a number of popular datasets aiming for facilitating the development of new segmentation algorithms.
Journal ArticleDOI
RGBD Salient Object Detection via Deep Fusion
TL;DR: A new convolutional neural network is designed to automatically learn the interaction mechanism for RGBD salient object detection by adopting various flexible and interpretable saliency feature vectors as inputs and integrates a superpixel-based Laplacian propagation framework with the trained CNN to extract a spatially consistent saliency map.