Enhancing Environmental Robustness in Few-shot Learning via Conditional Representation Learning
Journal:
arXiv
Published Date:
Feb 3, 2025
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.
