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Learning unbiased zero-shot semantic segmentation networks via transductive transfer
[article]
2020
arXiv
pre-print
Semantic segmentation, which aims to acquire a detailed understanding of images, is an essential issue in computer vision. However, in practical scenarios, new categories that are different from the categories in training usually appear. Since it is impractical to collect labeled data for all categories, how to conduct zero-shot learning in semantic segmentation establishes an important problem. Although the attribute embedding of categories can promote effective knowledge transfer across
arXiv:2007.00515v1
fatcat:qxgahnv6urgcpizsdcm6zc5xii