Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning

TL;DR

This paper introduces a new benchmark and a novel conditional representation learning network to improve the environmental robustness of few-shot learning models in complex real-world scenarios.

Contribution

The paper proposes CRLNet, a conditional representation learning approach, and introduces RD-FSL, a benchmark for evaluating environmental robustness in few-shot learning.

Findings

CRLNet outperforms existing methods by 6.83% to 16.98%.

Existing methods struggle with challenging test images in real-world environments.

The RD-FSL benchmark includes diverse challenging scenarios for evaluation.

Abstract

Few-shot learning (FSL) has recently been extensively utilized to overcome the scarcity of training data in domain-specific visual recognition. In real-world scenarios, environmental factors such as complex backgrounds, varying lighting conditions, long-distance shooting, and moving targets often cause test images to exhibit numerous incomplete targets or noise disruptions. However, current research on evaluation datasets and methodologies has largely ignored the concept of "environmental robustness", which refers to maintaining consistent performance in complex and diverse physical environments. This neglect has led to a notable decline in the performance of FSL models during practical testing compared to their training performance. To bridge this gap, we introduce a new real-world multi-domain few-shot learning (RD-FSL) benchmark, which includes four domains and six evaluation datasets. The test images in this benchmark feature various challenging elements, such as camouflaged objects, small targets, and blurriness. Our evaluation experiments reveal that existing methods struggle to utilize training images effectively to generate accurate feature representations for challenging test images. To address this problem, we propose a novel conditional representation learning network (CRLNet) that integrates the interactions between training and testing images as conditional information in their respective representation processes. The main goal is to reduce intra-class variance or enhance inter-class variance at the feature representation level. Finally, comparative experiments reveal that CRLNet surpasses the current state-of-the-art methods, achieving performance improvements ranging from 6.83% to 16.98% across diverse settings and backbones. The source code and dataset are available at https://github.com/guoqianyu-alberta/Conditional-Representation-Learning.