  Zero-shot learning (ZSL) for image classification focuses on recognizing
novel categories that have no labeled data available for training. The learning
is generally carried out with the help of mid-level semantic descriptors
associated with each class. This semantic-descriptor space is generally shared
by both seen and unseen categories. However, ZSL suffers from hubness, domain
discrepancy and biased-ness towards seen classes. To tackle these problems, we
propose a three-step approach to zero-shot learning. Firstly, a mapping is
learned from the semantic-descriptor space to the image-feature space. This
mapping learns to minimize both one-to-one and pairwise distances between
semantic embeddings and the image features of the corresponding classes.
Secondly, we propose test-time domain adaptation to adapt the semantic
embedding of the unseen classes to the test data. This is achieved by finding
correspondences between the semantic descriptors and the image features.
Thirdly, we propose scaled calibration on the classification scores of the seen
classes. This is necessary because the ZSL model is biased towards seen classes
as the unseen classes are not used in the training. Finally, to validate the
proposed three-step approach, we performed experiments on four benchmark
datasets where the proposed method outperformed previous results. We also
studied and analyzed the performance of each component of our proposed ZSL
framework.
