用粗标签训练模型，通过双阶段去偏提升细粒度识别能力
Twofold Debiasing Enhances Fine-Grained Learning with Coarse Labels
- 引入多层特征融合与中间层对齐，增强细粒度特征提取
- 在五个数据集上达到当前最佳性能，显著优于已有方法
- 适合细粒度分类中样本稀缺且仅有粗标签的场景
细粒度少样本学习（C2FS）任务要求仅使用粗粒度标签进行预训练，再利用少量子类样本实现细粒度识别。该任务面临两大挑战：粗粒度监督会抑制关键细粒度特征的提取；有限的细粒度样本导致模型过拟合于偏差分布。本文提出双阶段去偏（TFB）方法，通过特征增强与分布校准解决上述问题。具体地，设计多层特征融合重建模块与中间层特征对齐模块，缓解模型过度依赖粗粒度预测特征而忽略细粒度细节的问题；同时利用富含细粒度信息的粗粒度样本嵌入，校正细粒度分类器学习到的偏差分布。在五个基准数据集上的大量实验表明，该方法性能领先，显著超越现有方法。
原文摘要 · Abstract (English)
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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