Mask assembly for few-shot segmentation

Paper Title:

Visual and Textual Prior Guided Mask Assemble for Few-Shot Segmentation and Beyond

Published on:

15 August 2023

Primary Category:

Computer Vision and Pattern Recognition

Paper Authors:

Chen Shuai,

Meng Fanman,

Zhang Runtong,

Qiu Heqian,

Li Hongliang,

Wu Qingbo,

Xu Linfeng

Bullets

Key Details

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Proposes PGMA-Net, a class-agnostic model for few-shot segmentation

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Assembles masks from visual and textual priors via affinities

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Avoids bias towards base classes, excels on novel classes

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Achieves SOTA on PASCAL-5i and COCO-20i 1-shot tasks

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Can do zero-shot segmentation, co-segmentation without retraining

Explore the topics in this paper

class-agnostic processing

few-shot segmentation

mask prediction

visual and textual priors

zero-shot segmentation

AI generated summary

Mask assembly for few-shot segmentation

This paper proposes a new approach called Prior Guided Mask Assemble Network (PGMA-Net) for few-shot segmentation. It uses a class-agnostic process to assemble masks from visual and textual priors via affinities. This avoids bias towards base classes and allows the model to perform well on novel classes. The model achieves state-of-the-art results on PASCAL-5i and COCO-20i datasets in 1-shot scenario. It can also do related tasks like zero-shot segmentation and co-segmentation without retraining.