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Hierarchical Disentanglement of Discriminative Latent Features for Zero-Shot Learning
2019
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Most studies in zero-shot learning model the relationship, in the form of a classifier or mapping, between features from images of seen classes and their attributes. Therefore, the degree of a model's generalization ability for recognizing unseen images is highly constrained by that of image features and attributes. In this paper, we discuss two questions about generalization that are seldom discussed. Are image features trained with samples of seen classes expressive enough to capture the
doi:10.1109/cvpr.2019.01173
dblp:conf/cvpr/TongWKKN19
fatcat:yz3y47fohvczhkx6hho75phl24