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In computer vision applications, such as domain adaptation (DA), few shotlearning (FSL) and zero-shot learning (ZSL), we encounter new objects andenvironments, for which insufficient examples exist to allow for training"models from scratch," and methods that adapt existing models, trained on thepresented training environment, to the new scenario are required. We propose anovel visual attribute encoding method that encodes each image as alow-dimensional probability vector composed of prototypical part-typeprobabilities. The prototypes are learnt to be representative of all trainingdata. At test-time we utilize this encoding as an input to a classifier. Attest-time we freeze the encoder and only learn/adapt the classifier componentto limited annotated labels in FSL; new semantic attributes in ZSL. We conductextensive experiments on benchmark datasets. Our method outperformsstate-of-art methods trained for the specific contexts (ZSL, FSL, DA).

Learning Classifiers for Domain Adaptation, Zero and Few-Shot Recognition Based on Learning Latent Semantic Parts