Layer-Wise Feature Metric of Semantic-Pixel Matching for Few-Shot Learning

Humans have the ability to abstract and generalize low-level visual elements, such as contours, edges, colors, textures, and shapes, to form high-level semantic features that aid in recognizing and understanding the similarities and differences between objects. This capability is particularly crucial in few-shot classification tasks, as it allows models to accurately identify and distinguish between different categories based on contrasting critical high-level semantic features, even when faced with a limited number of samples from new categories. In contrast, traditional deep learning methods [1, 2] typically rely on large amounts of labeled data for training in order to recognize and classify specific objects or concepts. In few-shot learning scenarios, these models may encounter challenges, as they are not specifically designed to learn from a small amount of data quickly. Recently, few-shot learning methods have been introduced to address this limitation, typically requiring only a few images to understand the characteristics of a class and generalize these features to unseen images for inductive reasoning.