Learning fine-grained embeddings from coarse labels is a challenging task due to limited label granularity supervision, i.e., lacking the detailed distinctions required for fine-grained tasks. The task becomes even more demanding when attempting few-shot fine-grained recognition, which holds practical significance in various applications. To address these challenges, we propose a novel method that embeds visual embeddings into a hyperbolic space and enhances their discriminative ability with a hierarchical cosine margins manner. Specifically, the hyperbolic space offers distinct advantages, including the ability to capture hierarchical relationships and increased expressive power, which favors modeling fine-grained objects. Based on the hyperbolic space, we further enforce relatively large/small similarity margins between coarse/fine classes, respectively, yielding the so-called hierarchical cosine margins manner. While enforcing similarity margins in the regular Euclidean space has become popular for deep embedding learning, applying it to the hyperbolic space is non-trivial and validating the benefit for coarse-to-fine generalization is valuable. Extensive experiments conducted on five benchmark datasets showcase the effectiveness of our proposed method, yielding state-of-the-art results surpassing competing methods.

翻译：从粗标签中学习细粒度嵌入是一项具有挑战性的任务，原因在于标签粒度监督有限，即缺乏细粒度任务所需的详细区分。当尝试进行在多种应用中具有实际意义的小样本细粒度识别时，该任务变得更加困难。为应对这些挑战，我们提出了一种新颖方法，该方法将视觉嵌入映射到双曲空间中，并通过分层余弦边界方式增强其判别能力。具体而言，双曲空间具有显著优势，包括能够捕捉层次关系以及更强的表达能力，这有利于对细粒度对象进行建模。基于双曲空间，我们进一步在粗/细类别之间分别施加相对较大/较小的相似性边界，从而形成所谓的分层余弦边界方式。虽然在常规欧几里得空间中施加相似性边界在深度嵌入学习中已变得流行，但将其应用于双曲空间并非易事，而验证其对粗到细泛化的益处具有重要价值。在五个基准数据集上进行的大量实验展示了我们提出方法的有效性，其性能超越了现有竞争方法，达到了最先进水平。