  Few-shot learning methods offer pre-training techniques optimized for easier
later adaptation of the model to new classes (unseen during training) using one
or a few examples. This adaptivity to unseen classes is especially important
for many practical applications where the pre-trained label space cannot remain
fixed for effective use and the model needs to be "specialized" to support new
categories on the fly. One particularly interesting scenario, essentially
overlooked by the few-shot literature, is Coarse-to-Fine Few-Shot (C2FS), where
the training classes (e.g. animals) are of much `coarser granularity' than the
target (test) classes (e.g. breeds). A very practical example of C2FS is when
the target classes are sub-classes of the training classes. Intuitively, it is
especially challenging as (both regular and few-shot) supervised pre-training
tends to learn to ignore intra-class variability which is essential for
separating sub-classes. In this paper, we introduce a novel 'Angular
normalization' module that allows to effectively combine supervised and
self-supervised contrastive pre-training to approach the proposed C2FS task,
demonstrating significant gains in a broad study over multiple baselines and
datasets. We hope that this work will help to pave the way for future research
on this new, challenging, and very practical topic of C2FS classification.

Computer Vision and Pattern Recognition