Local Descriptors Weighted Adaptive Threshold Filtering For Few-Shot Learning

TL;DR

This paper introduces a weighted adaptive threshold filtering strategy for local descriptors in few-shot image classification, improving category relevance focus and noise filtering without extra learnable parameters.

Contribution

The proposed WATF method adaptively filters local descriptors based on context, enhancing classification accuracy in few-shot learning without increasing model complexity.

Findings

Improves clustering of local descriptors

Enhances discriminative ability between categories

Maintains lightweight design without extra parameters

Abstract

Few-shot image classification is a challenging task in the field of machine learning, involving the identification of new categories using a limited number of labeled samples. In recent years, methods based on local descriptors have made significant progress in this area. However, the key to improving classification accuracy lies in effectively filtering background noise and accurately selecting critical local descriptors highly relevant to image category information. To address this challenge, we propose an innovative weighted adaptive threshold filtering (WATF) strategy for local descriptors. This strategy can dynamically adjust based on the current task and image context, thereby selecting local descriptors most relevant to the image category. This enables the model to better focus on category-related information while effectively mitigating interference from irrelevant background regions. To evaluate the effectiveness of our method, we adopted the N-way K-shot experimental framework. Experimental results show that our method not only improves the clustering effect of selected local descriptors but also significantly enhances the discriminative ability between image categories. Notably, our method maintains a simple and lightweight design philosophy without introducing additional learnable parameters. This feature ensures consistency in filtering capability during both training and testing phases, further enhancing the reliability and practicality of the method.