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Learning novel classes from a very few labeled samples has attractedincreasing attention in machine learning areas. Recent research on eithermeta-learning based or transfer-learning based paradigm demonstrates thatgaining information on a good feature space can be an effective solution toachieve favorable performance on few-shot tasks. In this paper, we propose asimple but effective paradigm that decouples the tasks of learning featurerepresentations and classifiers and only learns the feature embeddingarchitecture from base classes via the typical transfer-learning trainingstrategy. To maintain both the generalization ability across base and novelclasses and discrimination ability within each class, we propose a dual pathfeature learning scheme that effectively combines structural similarity withcontrastive feature construction. In this way, both inner-class alignment andinter-class uniformity can be well balanced, and result in improvedperformance. Experiments on three popular benchmarks show that whenincorporated with a simple prototype based classifier, our method can stillachieve promising results for both standard and generalized few-shot problemsin either an inductive or transductive inference setting.

Dual Path Structural Contrastive Embeddings for Learning Novel Objects