  Few-shot segmentation aims to train a segmentation model that can fast adapt
to a novel task for which only a few annotated images are provided. Most recent
models have adopted a prototype-based paradigm for few-shot inference. These
approaches may have limited generalization capacity beyond the standard 1- or
5-shot settings. In this paper, we closely examine and reevaluate the
fine-tuning based learning scheme that fine-tunes the classification layer of a
deep segmentation network pre-trained on diverse base classes. To improve the
generalizability of the classification layer optimized with sparsely annotated
samples, we introduce an instance-aware data augmentation (IDA) strategy that
augments the support images based on the relative sizes of the target objects.
The proposed IDA effectively increases the support set's diversity and promotes
the distribution consistency between support and query images. On the other
hand, the large visual difference between query and support images may hinder
knowledge transfer and cripple the segmentation performance. To cope with this
challenge, we introduce the local consensus guided cross attention (LCCA) to
align the query feature with support features based on their dense correlation,
further improving the model's generalizability to the query image. The
significant performance improvements on the standard few-shot segmentation
benchmarks PASCAL-$5^i$ and COCO-$20^i$ verify the efficacy of our proposed
method.
