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Successful semantic segmentation methods typically rely on the training datasets containing a large number of pixel-wise labeled images. To alleviate the dependence on such a fully annotated training dataset, in this paper, we propose a semi- and weakly-...
Shadow detection is undergoing a rapid and remarkable development along with the wide use of deep neural networks. Benefiting from a large number of training images annotated with strong pixel-level ground-truth masks, current deep shadow ...
Meta-learning is a machine learning paradigm that extracts crosstask knowledge by learning a large number of subtasks, to fast adapt to new tasks. Many meta-learning methods are widely applied in few-shot classification. These methods adopt an episodic ...
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