Single-Step Support Set Mining for Realistic Few-Shot Image Classification
Chao Ye, Qian Wang, Lanfang Dong · 2024
Traditional few-shot learning (FSL) methods, often based on N-way K-shot classification, typically assume access to a large amount of labelled base classes and a class-balanced support set, which are not always feasible in real-world applications. This assumption limits the applicability of these methods in scenarios where data is scarce or imbalanced. The lack of base classes prevents the meta-training for generalized image feature extraction. We investigate the efficacy of using pre-trained models for feature extraction in practical FSL tasks, exploring how these models can compensate for the limited data availability. On the other hand, annotating K samples for each class to form the class-balanced support set is non-trivial, given that the ground truth label is unknown before annotating actually happens. This challenge highlights the need for efficient annotation strategies in FSL. This raises an overlooked research question how we can efficiently select samples to annotate to form the support set for FSL. To address this, we introduce and compare various Single-Step Support Set Mining (S4M) strategies for efficient data labeling. The proposed S4M enhances the relevance and representativeness of the selected samples, offering a more data-driven strategy for optimizing sample selection. This approach is particularly beneficial in reducing the annotation burden and improving the quality of the support set. To validate the effectiveness of proposed S4M strategies, we conduct extensive empirical studies on the transductive few-shot image classification tasks by incorporating S4M into a state-of-the-art transductive FSL framework. Our experimental results on benchmark and realistic datasets demonstrate the effectiveness of S4M under various scenarios, marking it as a practical and efficient choice for few-shot image classification in real-world applications, especially in situations where data is limited or imbalanced.