Beyond the Prototype: Divide-and-conquer Proxies for Few-shot Segmentation

by   Chunbo Lang, et al.

Few-shot segmentation, which aims to segment unseen-class objects given only a handful of densely labeled samples, has received widespread attention from the community. Existing approaches typically follow the prototype learning paradigm to perform meta-inference, which fails to fully exploit the underlying information from support image-mask pairs, resulting in various segmentation failures, e.g., incomplete objects, ambiguous boundaries, and distractor activation. To this end, we propose a simple yet versatile framework in the spirit of divide-and-conquer. Specifically, a novel self-reasoning scheme is first implemented on the annotated support image, and then the coarse segmentation mask is divided into multiple regions with different properties. Leveraging effective masked average pooling operations, a series of support-induced proxies are thus derived, each playing a specific role in conquering the above challenges. Moreover, we devise a unique parallel decoder structure that integrates proxies with similar attributes to boost the discrimination power. Our proposed approach, named divide-and-conquer proxies (DCP), allows for the development of appropriate and reliable information as a guide at the "episode" level, not just about the object cues themselves. Extensive experiments on PASCAL-5i and COCO-20i demonstrate the superiority of DCP over conventional prototype-based approaches (up to 5 10 which also establishes a new state-of-the-art. Code is available at


page 1

page 3

page 4

page 6


Self-Guided and Cross-Guided Learning for Few-Shot Segmentation

Few-shot segmentation has been attracting a lot of attention due to its ...

Semantically Meaningful Class Prototype Learning for One-Shot Image Semantic Segmentation

One-shot semantic image segmentation aims to segment the object regions ...

MSANet: Multi-Similarity and Attention Guidance for Boosting Few-Shot Segmentation

Few-shot segmentation aims to segment unseen-class objects given only a ...

Self-supervised Image-specific Prototype Exploration for Weakly Supervised Semantic Segmentation

Weakly Supervised Semantic Segmentation (WSSS) based on image-level labe...

SG-One: Similarity Guidance Network for One-Shot Semantic Segmentation

One-shot semantic segmentation poses a challenging task of recognizing t...

Learning What Not to Segment: A New Perspective on Few-Shot Segmentation

Recently few-shot segmentation (FSS) has been extensively developed. Mos...

Adaptive Prototype Learning and Allocation for Few-Shot Segmentation

Prototype learning is extensively used for few-shot segmentation. Typica...

Please sign up or login with your details

Forgot password? Click here to reset