  Current few-shot learning models capture visual object relations in the
so-called meta-learning setting under a fixed-resolution input. However, such
models have a limited generalization ability under the scale and location
mismatch between objects, as only few samples from target classes are provided.
Therefore, the lack of a mechanism to match the scale and location between
pairs of compared images leads to the performance degradation. The importance
of image contents varies across coarse-to-fine scales depending on the object
and its class label, e.g., generic objects and scenes rely on their global
appearance while fine-grained objects rely more on their localized visual
patterns. In this paper, we study the impact of scale and location mismatch in
the few-shot learning scenario, and propose a novel Spatially-aware Matching
(SM) scheme to effectively perform matching across multiple scales and
locations, and learn image relations by giving the highest weights to the best
matching pairs. The SM is trained to activate the most related locations and
scales between support and query data. We apply and evaluate SM on various
few-shot learning models and backbones for comprehensive evaluations.
Furthermore, we leverage an auxiliary self-supervisory discriminator to
train/predict the spatial- and scale-level index of feature vectors we use.
Finally, we develop a novel transformer-based pipeline to exploit self- and
cross-attention in a spatially-aware matching process. Our proposed design is
orthogonal to the choice of backbone and/or comparator.
