  The few-shot learning ability of vision transformers (ViTs) is rarely
investigated though heavily desired. In this work, we empirically find that
with the same few-shot learning frameworks, \eg~Meta-Baseline, replacing the
widely used CNN feature extractor with a ViT model often severely impairs
few-shot classification performance. Moreover, our empirical study shows that
in the absence of inductive bias, ViTs often learn the low-qualified token
dependencies under few-shot learning regime where only a few labeled training
data are available, which largely contributes to the above performance
degradation. To alleviate this issue, for the first time, we propose a simple
yet effective few-shot training framework for ViTs, namely Self-promoted
sUpervisioN (SUN). Specifically, besides the conventional global supervision
for global semantic learning SUN further pretrains the ViT on the few-shot
learning dataset and then uses it to generate individual location-specific
supervision for guiding each patch token. This location-specific supervision
tells the ViT which patch tokens are similar or dissimilar and thus accelerates
token dependency learning. Moreover, it models the local semantics in each
patch token to improve the object grounding and recognition capability which
helps learn generalizable patterns. To improve the quality of location-specific
supervision, we further propose two techniques:~1) background patch filtration
to filtrate background patches out and assign them into an extra background
class; and 2) spatial-consistent augmentation to introduce sufficient diversity
for data augmentation while keeping the accuracy of the generated local
supervisions. Experimental results show that SUN using ViTs significantly
surpasses other few-shot learning frameworks with ViTs and is the first one
that achieves higher performance than those CNN state-of-the-arts.
