  Few-shot learning aims to recognize novel classes with few examples.
Pre-training based methods effectively tackle the problem by pre-training a
feature extractor and then fine-tuning it through the nearest centroid based
meta-learning. However, results show that the fine-tuning step makes marginal
improvements. In this paper, 1) we figure out the reason, i.e., in the
pre-trained feature space, the base classes already form compact clusters while
novel classes spread as groups with large variances, which implies that
fine-tuning feature extractor is less meaningful; 2) instead of fine-tuning
feature extractor, we focus on estimating more representative prototypes.
Consequently, we propose a novel prototype completion based meta-learning
framework. This framework first introduces primitive knowledge (i.e.,
class-level part or attribute annotations) and extracts representative features
for seen attributes as priors. Second, a part/attribute transfer network is
designed to learn to infer the representative features for unseen attributes as
supplementary priors. Finally, a prototype completion network is devised to
learn to complete prototypes with these priors. Moreover, to avoid the
prototype completion error, we further develop a Gaussian based prototype
fusion strategy that fuses the mean-based and completed prototypes by
exploiting the unlabeled samples. Extensive experiments show that our method:
(i) obtains more accurate prototypes; (ii) achieves superior performance on
both inductive and transductive FSL settings.
