Few-Shot Segmentation via Divide-and-Conquer Proxies

Few-Shot segmentation (FSS) is a marginally explored but challenging task that aims to identify unseen classes of objects with only a handful of densely annotated samples. By and large, current FSS approaches perform meta-inference based on the prototype learning paradigm, which fails to fully explo...

Full description

Saved in:
Bibliographic Details
Published in:International journal of computer vision 2024, Vol.132 (1), p.261-283
Main Authors: Lang, Chunbo, Cheng, Gong, Tu, Binfei, Han, Junwei
Format: Article
Language:English
Subjects:
Artificial Intelligence
Computer Imaging
Computer Science
Computer vision
Effectiveness
Image contrast
Image enhancement
Image processing
Image Processing and Computer Vision
Image segmentation
Pattern Recognition
Pattern Recognition and Graphics
Propagation
Prototypes
Proxy
Regularization
Semantics
Vision
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Few-Shot segmentation (FSS) is a marginally explored but challenging task that aims to identify unseen classes of objects with only a handful of densely annotated samples. By and large, current FSS approaches perform meta-inference based on the prototype learning paradigm, which fails to fully exploit the underlying information from support image-mask pairs, resulting in multiple segmentation failures, such as incomplete objects, ambiguous boundaries, and distractor activation. For this purpose, a flexible and generic framework is developed in the spirit of divide-and-conquer. We first implement a novel self-reasoning scheme on the labeled support image, and then divide the coarse segmentation mask into several regions with different properties. By employing effective masked average pooling techniques, a series of support-induced proxies are generated on the fly, each performing a specific role in conquering the above challenges. Furthermore, we meticulously devise the parallel decoder structure and semantic consistency regularization to eliminate confusion and enhance discrimination. In stark contrast to conventional prototype-based approaches, our proposed divide-and-conquer proxies (DCP) can provide “episode” level guidelines that go well beyond the object cues themselves. Extensive experiments are conducted on FSS benchmarks to verify the effectiveness, including standard settings as well as cross-domain settings. In particular, we propose a temporal DCP and successfully extend it to video object segmentation via memory repository and progressive propagation, illustrating the high scalability. The source codes are available at https://github.com/chunbolang/DCP .
ISSN:0920-5691
1573-1405
DOI:10.1007/s11263-023-01886-8