  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.
