Abstract
Few-shot image classification consists of two consecutive learning processes: 1) In the meta-learning stage, the model acquires a knowledge base from a set of training classes. 2) During meta-testing, the acquired knowledge is used to recognize unseen classes from very few examples. Inspired by the compositional representation of objects in humans, we train a neural network architecture that explicitly represents objects as a set of parts and their spatial composition. In particular, during meta-learning, we train a knowledge base that consists of a dictionary of part representations and a dictionary of part activation maps that encode frequent spatial activation patterns of parts. The elements of both dictionaries are shared among the training classes. During meta-testing, the representation of unseen classes is learned using the part representations and the part activation maps from the knowledge base. Finally, an attention mechanism is used to strengthen those parts that are most important for each category. We demonstrate the value of our compositional learning framework for a few-shot classification using miniImageNet, tieredImageNet, CIFAR-FS, and FC100, where we achieve state-of-the-art performance.
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URL
https://arxiv.org/abs/2101.11878
