  Few-shot learning (FSL) targets at generalization of vision models towards
unseen tasks without sufficient annotations. Despite the emergence of a number
of few-shot learning methods, the sample selection bias problem, i.e., the
sensitivity to the limited amount of support data, has not been well
understood. In this paper, we find that this problem usually occurs when the
positions of support samples are in the vicinity of task centroid -- the mean
of all class centroids in the task. This motivates us to propose an extremely
simple feature transformation to alleviate this problem, dubbed Task Centroid
Projection Removing (TCPR). TCPR is applied directly to all image features in a
given task, aiming at removing the dimension of features along the direction of
the task centroid. While the exact task centroid cannot be accurately obtained
from limited data, we estimate it using base features that are each similar to
one of the support features. Our method effectively prevents features from
being too close to the task centroid. Extensive experiments over ten datasets
from different domains show that TCPR can reliably improve classification
accuracy across various feature extractors, training algorithms and datasets.
The code has been made available at https://github.com/KikimorMay/FSL-TCBR.
