Weakly-supervised Object Localization for Few-shot Learning and Fine-grained Few-shot Learning

TL;DR

This paper introduces a weakly-supervised object localization approach using a Self-Attention Based Complementary Module and Semantic Alignment Module, significantly improving few-shot learning, especially for fine-grained categories, with strong generalization and localization results.

Contribution

It proposes a novel weakly-supervised localization framework combining SAC and SAM modules to enhance few-shot and fine-grained classification performance.

Findings

Outperforms state-of-the-art methods on benchmark datasets.

Achieves superior results in fine-grained few-shot tasks.

Demonstrates strong generalization across datasets.

Abstract

Few-shot learning (FSL) aims to learn novel visual categories from very few samples, which is a challenging problem in real-world applications. Many methods of few-shot classification work well on general images to learn global representation. However, they can not deal with fine-grained categories well at the same time due to a lack of subtle and local information. We argue that localization is an efficient approach because it directly provides the discriminative regions, which is critical for both general classification and fine-grained classification in a low data regime. In this paper, we propose a Self-Attention Based Complementary Module (SAC Module) to fulfill the weakly-supervised object localization, and more importantly produce the activated masks for selecting discriminative deep descriptors for few-shot classification. Based on each selected deep descriptor, Semantic Alignment Module (SAM) calculates the semantic alignment distance between the query and support images to boost classification performance. Extensive experiments show our method outperforms the state-of-the-art methods on benchmark datasets under various settings, especially on the fine-grained few-shot tasks. Besides, our method achieves superior performance over previous methods when training the model on miniImageNet and evaluating it on the different datasets, demonstrating its superior generalization capacity. Extra visualization shows the proposed method can localize the key objects more interval.