A-FSL: Adaptive Few-Shot Learning via Task-Driven Context Aggregation and Attentive Feature Refinement

Learning new categories with limited training samples presents a significant challenge for conventional deep learning frameworks. The few-shot learning (FSL) paradigm emerges as a potential solution to address practical constraints in this challenge. The primary difficulties in FSL are insufficient prior knowledge and ineffective alignment of clusters to their corresponding classification vectors in the pretrained feature space. While many FSL methods employ task-agnostic instances and class-specific embedding functions, we argue that incorporating task-specific knowledge is crucial for overcoming FSL challenges. To achieve adaptability in FSL, we propose an Adaptive Few-Shot Learning (A-FSL) framework which (1) aggregates task-specific knowledge and adapts the classification vectors in the pretrained feature space and (2) develops a query class correlation attention module to enhance cluster formation. By considering task-specific information at multiple scales of visual features, we can overcome the limitations of a fixed feature space and refine it to adapt classification and query vectors effectively. The A-FSL framework leads to well-formed clusters for novel classes where classification vectors are drawn toward the clusters, even in the 1-shot setting. Through comprehensive experimental evaluation, we show that our method outperforms the current state-of-the-art on benchmark datasets.