



Zero-shot is a transfer learning paradigm to address the problem where training data is not available for some classes.
Zero-shot learning aims to train a model that can classify text/objects of unseen classes (classes unavailable during training) via transferring knowledge obtained from other seen classes during training with the help of visual and semantic information.