Dual Path Structural Contrastive Embeddings for Learning Novel Objects

• URL: http://arxiv.org/abs/2112.12359v2
• Date: Fri, 24 Dec 2021 08:52:57 GMT
• Title: Dual Path Structural Contrastive Embeddings for Learning Novel Objects
• Authors: Bingbin Li, Elvis Han Cui, Yanan Li, Donghui Wang, Weng Kee Wong
• Abstract summary: Recent research shows that gaining information on a good feature space can be an effective solution to achieve favorable performance on few-shot tasks. We propose a simple but effective paradigm that decouples the tasks of learning feature representations and classifiers. Our method can still achieve promising results for both standard and generalized few-shot problems in either an inductive or transductive inference setting.
• Score: 6.979491536753043
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Learning novel classes from a very few labeled samples has attracted increasing attention in machine learning areas. Recent research on either meta-learning based or transfer-learning based paradigm demonstrates that gaining information on a good feature space can be an effective solution to achieve favorable performance on few-shot tasks. In this paper, we propose a simple but effective paradigm that decouples the tasks of learning feature representations and classifiers and only learns the feature embedding architecture from base classes via the typical transfer-learning training strategy. To maintain both the generalization ability across base and novel classes and discrimination ability within each class, we propose a dual path feature learning scheme that effectively combines structural similarity with contrastive feature construction. In this way, both inner-class alignment and inter-class uniformity can be well balanced, and result in improved performance. Experiments on three popular benchmarks show that when incorporated with a simple prototype based classifier, our method can still achieve promising results for both standard and generalized few-shot problems in either an inductive or transductive inference setting.

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