Revisiting the Transferability of Few-Shot Image Classification: A Frequency Spectrum Perspective
Min Zhang, Zhitao Wang, Donglin Wang
TL;DR
This paper explores how different frequency components in images affect few-shot image classification performance and introduces a new method to improve transferability.
Contribution
The paper introduces a novel frequency spectrum perspective and a method called Frequency Spectrum Mask (FRSM) to enhance transferability in few-shot image classification.
Findings
Non-causal frequencies like background information negatively impact transferability in few-shot learning.
The proposed FRSM method improves transferability by weighting and mitigating non-causal frequencies.
Experiments show FRSM significantly enhances performance across nine datasets.
Abstract
Few-shot learning, especially few-shot image classification (FSIC), endeavors to recognize new categories using only a handful of labeled images by transferring knowledge from a model trained on base categories. Despite numerous efforts to address the challenge of deficient transferability caused by the distribution shift between the base and new classes, the fundamental principles remain a subject of debate. In this paper, we elucidate why a decline in performance occurs and what information is transferred during the testing phase, examining it from a frequency spectrum perspective. Specifically, we adopt causality on the frequency space for FSIC. With our causal assumption, non-causal frequencies (e.g., background knowledge) act as confounders between causal frequencies (e.g., object information) and predictions. Our experimental results reveal that different frequency components…
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Taxonomy
TopicsDomain Adaptation and Few-Shot Learning · Image Processing Techniques and Applications · Photoacoustic and Ultrasonic Imaging