Rethinking the Correlation in Few-Shot Segmentation: A Buoys View

Yuan Wang

Abstract:

Few-shot segmentation (FSS) aims to segment novel objects in a given query image with only a few annotated
support images. However, most previous best-performing
methods, whether prototypical learning methods or affinity
learning methods, neglect to alleviate false matches caused
by their own pixel-level correlation. In this work, we rethink
how to mitigate the false matches from the perspective of
representative reference features (referred to as buoys), and
propose a novel adaptive buoys correlation (ABC) network
to rectify direct pairwise pixel-level correlation, including a
buoys mining module and an adaptive correlation module.
The proposed ABC enjoys several merits. First, to learn
the buoys well without any correspondence supervision, we
customize the buoys mining module according to the three
characteristics of representativeness, task awareness and resilience. Second, the proposed adaptive correlation module
is responsible for further endowing buoy-correlation-based
pixel matching with an adaptive ability. Extensive experimental results with two different backbones on two challenging
benchmarks demonstrate that our ABC, as a general plugin, achieves consistent improvements over several leading
methods on both 1-shot and 5-shot settings