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Zero-Shot Recognition via Semantic Embeddings and Knowledge Graphs
2018
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
We consider the problem of zero-shot recognition: learning a visual classifier for a category with zero training examples, just using the word embedding of the category and its relationship to other categories, which visual data are provided. The key to dealing with the unfamiliar or novel category is to transfer knowledge obtained from familiar classes to describe the unfamiliar class. In this paper, we build upon the recently introduced Graph Convolutional Network (GCN) and propose an
doi:10.1109/cvpr.2018.00717
dblp:conf/cvpr/0004YG18
fatcat:uh3n5xgf3fc7xn7uxufyn74ipu