通过聚类生成细粒度伪标签，缓解植物分类中的类内差异问题。
FGDCC: Fine-Grained Deep Cluster Categorization -- A Framework for Intra-Class Variability Problems in Plant Classification
- 对每类植物图像单独聚类，生成隐含相似性的伪标签。
- 在PlantNet300k上达到当前最优性能，即使部分组件未完全优化。
- 适合解决细粒度植物识别中样本差异大、类别少的难题。
类内差异指同一类别中图像间的差异程度，其强度可能阻碍深度学习模型的学习，尤其当类别样本稀少时——这在细粒度视觉分类（FGVC）中极为常见。本文提出FGDCC框架，通过为每个类别独立聚类，发现能编码图像间潜在相似性的伪标签，并用于分层分类，从而学习更精细的视觉特征，缓解类内差异问题。在PlantNet300k上的初步实验揭示了未来研究方向，尽管部分组件未充分优化，该方法仍取得了当前最优结果。代码已开源。
原文摘要 · Abstract (English)
Intra-class variability is given according to the significance in the degree of dissimilarity between images within a class. In that sense, depending on its intensity, intra-class variability can hinder the learning process for DL models, specially when such classes are also underrepresented, which is a very common scenario in Fine-Grained Visual Categorization (FGVC) tasks. This paper proposes a novel method that aims at leveraging classification performance in FGVC tasks by learning fine-grained features via classification of class-wise cluster assignments. Our goal is to apply clustering over each class individually, which can allow to discover pseudo-labels that encodes a latent degree of similarity between images. In turn, those labels can be employed in a hierarchical classification process that allows to learn more fine-grained visual features and thereby mitigating intra-class variability issues. Initial experiments over the PlantNet300k enabled to shed light upon several key points in which future work will have to be developed in order to find more conclusive evidence regarding the effectiveness of our method. Our method still achieves state-of-the-art performance on the PlantNet300k dataset even though some of its components haven't been shown to be fully optimized. Our code is available at \href{https://github.com/ADAM-UEFS/FGDCC}{https://github.com/ADAM-UEFS/FGDCC}.
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