  We show that the way inference is performed in few-shot segmentation tasks
has a substantial effect on performances -- an aspect often overlooked in the
literature in favor of the meta-learning paradigm. We introduce a transductive
inference for a given query image, leveraging the statistics of its unlabeled
pixels, by optimizing a new loss containing three complementary terms: i) the
cross-entropy on the labeled support pixels; ii) the Shannon entropy of the
posteriors on the unlabeled query-image pixels; and iii) a global KL-divergence
regularizer based on the proportion of the predicted foreground. As our
inference uses a simple linear classifier of the extracted features, its
computational load is comparable to inductive inference and can be used on top
of any base training. Foregoing episodic training and using only standard
cross-entropy training on the base classes, our inference yields competitive
performances on standard benchmarks in the 1-shot scenarios. As the number of
available shots increases, the gap in performances widens: on PASCAL-5i, our
method brings about 5% and 6% improvements over the state-of-the-art, in the 5-
and 10-shot scenarios, respectively. Furthermore, we introduce a new setting
that includes domain shifts, where the base and novel classes are drawn from
different datasets. Our method achieves the best performances in this more
realistic setting. Our code is freely available online:
https://github.com/mboudiaf/RePRI-for-Few-Shot-Segmentation.
