Plant leaf identification is crucial for biodiversity protection and conservation and has gradually attracted the attention of academia in recent years. Due to the high similarity among different varieties, leaf cultivar recognition is also considered to be an ultra-fine-grained visual classification (UFGVC) task, which is facing a huge challenge. In practice, an instance may be related to multiple varieties to varying degrees, especially in the UFGVC datasets. However, deep learning methods trained on one-hot labels fail to reflect patterns shared across categories and thus perform poorly on this task. To address this issue, we generate soft targets integrated with inter-class similarity information. Specifically, we continuously update the prototypical features for each category and then capture the similarity scores between instances and prototypes accordingly. Original one-hot labels and the similarity scores are incorporated to yield enhanced labels. Prototype-enhanced soft labels not only contain original one-hot label information, but also introduce rich inter-category semantic association information, thus providing more effective supervision for deep model training. Extensive experimental results on public datasets show that our method can significantly improve the performance on the UFGVC task of leaf cultivar identification.

翻译：植物叶片识别对于生物多样性保护至关重要，近年来逐渐引起学术界关注。由于不同品种间高度相似性，叶片品种识别被视为超细粒度视觉分类（UFGVC）任务，面临巨大挑战。实践中，一个实例可能与多个品种存在不同程度关联，尤其在UFGVC数据集中。然而，基于独热标签训练的深度学习方法无法反映跨类别共享模式，因此在此任务中表现不佳。为解决该问题，我们生成融合类间相似度信息的软标签。具体而言，我们持续更新每个类别的原型特征，进而捕获实例与原型之间的相似度分数。将原始独热标签与相似度分数融合，生成增强标签。原型增强软标签不仅保留原始独热标签信息，还引入丰富的跨类别语义关联信息，从而为深度模型训练提供更有效的监督。在公开数据集上的大量实验结果表明，我们的方法能显著提升叶片品种识别这一UFGVC任务的性能。