Zero-shot image classification is the task of classifying previously unseen classes during training of a model.
About the Task
Zero-shot image classification is a computer vision task to classify images into one of several classes, without any prior training or knowledge of the classes.
Zero shot image classification works by transferring knowledge learnt during training of one model, to classify novel classes that was not present in the training data. So this is a variation of transfer learning. For instance, a model trained to differentiate cars from airplanes can be used to classify images of ships.
The data in this learning paradigm consists of
Seen data - images and their corresponding labels
Unseen data - only labels and no images
Auxiliary information - additional information given to the model during training connecting the unseen and seen data. This can be in the form of textual description or word embeddings.
About the Task
Zero-shot image classification is a computer vision task to classify images into one of several classes, without any prior training or knowledge of the classes.
Zero shot image classification works by transferring knowledge learnt during training of one model, to classify novel classes that was not present in the training data. So this is a variation of transfer learning. For instance, a model trained to differentiate cars from airplanes can be used to classify images of ships.
The data in this learning paradigm consists of
Seen data - images and their corresponding labels
Unseen data - only labels and no images
Auxiliary information - additional information given to the model during training connecting the unseen and seen data. This can be in the form of textual description or word embeddings.