What Is Zero Shot Learning in Computer Vision?

Zero-Shot Learning (ZSL) is a machine learning technique that enables models to classify objects from categories they have not been specifically trained on by utilizing auxiliary information, such as text descriptions, to infer what might be in an image. This method is particularly beneficial in overcoming the challenges associated with data labeling, which can be costly and time-consuming, especially in specialized fields where expert annotations are scarce. ZSL is a subset of transfer learning, specifically heterogeneous transfer learning, wherein the feature and label spaces differ. The technique involves pre-training a model on seen classes and then leveraging semantic information to generalize to unseen classes. Zero-Shot Learning has various applications, including image classification, object detection, and natural language processing, and can be implemented using methods such as classifier-based and instance-based approaches. Despite its advantages, ZSL faces limitations like bias, domain shift, hubness, and semantic loss due to the differences between training and testing data distributions.