Twofold Debiasing Enhances Fine-Grained Learning with Coarse Labels

• URL: http://arxiv.org/abs/2502.19816v1
• Date: Thu, 27 Feb 2025 06:41:49 GMT
• Title: Twofold Debiasing Enhances Fine-Grained Learning with Coarse Labels
• Authors: Xin-yang Zhao, Jian Jin, Yang-yang Li, Yazhou Yao,
• Abstract summary: coarse-grained supervised pre-training suppresses the extraction of critical fine-grained features for discrimination.<n>Models suffer from overfitting due to biased distributions caused by limited fine-grained samples.<n>We propose the Twofold Debiasing (TFB) method, which addresses these challenges through detailed feature enhancement and distribution calibration.
• Score: 22.58772008332314
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Coarse-to-Fine Few-Shot (C2FS) task is designed to train models using only coarse labels, then leverages a limited number of subclass samples to achieve fine-grained recognition capabilities. This task presents two main challenges: coarse-grained supervised pre-training suppresses the extraction of critical fine-grained features for subcategory discrimination, and models suffer from overfitting due to biased distributions caused by limited fine-grained samples. In this paper, we propose the Twofold Debiasing (TFB) method, which addresses these challenges through detailed feature enhancement and distribution calibration. Specifically, we introduce a multi-layer feature fusion reconstruction module and an intermediate layer feature alignment module to combat the model's tendency to focus on simple predictive features directly related to coarse-grained supervision, while neglecting complex fine-grained level details. Furthermore, we mitigate the biased distributions learned by the fine-grained classifier using readily available coarse-grained sample embeddings enriched with fine-grained information. Extensive experiments conducted on five benchmark datasets demonstrate the efficacy of our approach, achieving state-of-the-art results that surpass competitive methods.

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