  Few-shot semantic segmentation aims at learning to segment a target object
from a query image using only a few annotated support images of the target
class. This challenging task requires to understand diverse levels of visual
cues and analyze fine-grained correspondence relations between the query and
the support images. To address the problem, we propose Hypercorrelation Squeeze
Networks (HSNet) that leverages multi-level feature correlation and efficient
4D convolutions. It extracts diverse features from different levels of
intermediate convolutional layers and constructs a collection of 4D correlation
tensors, i.e., hypercorrelations. Using efficient center-pivot 4D convolutions
in a pyramidal architecture, the method gradually squeezes high-level semantic
and low-level geometric cues of the hypercorrelation into precise segmentation
masks in coarse-to-fine manner. The significant performance improvements on
standard few-shot segmentation benchmarks of PASCAL-5i, COCO-20i, and FSS-1000
verify the efficacy of the proposed method.
