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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 thedoi:10.1109/cvpr.2019.01173 dblp:conf/cvpr/TongWKKN19 fatcat:yz3y47fohvczhkx6hho75phl24