Few-shot segmentation based on multi-level and cross-scale clustering

IF 3.2 4区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Connection Science Pub Date : 2024-02-29 DOI:10.1080/09540091.2023.2287972

Shuai Yuan, Junhai Qiu, Hongxia Xu, Yan Zhang, Jiaxing Zhang

引用次数: 0

Abstract

The problem of image segmentation with few-shot learning is addressed in this paper, which is a challenging task due to the lack of sufficient high-precision annotated data. A novel method that con...

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基于多层次和跨尺度聚类技术的微镜头分割技术

由于缺乏足够的高精度注释数据，图像分割是一项极具挑战性的任务。一种新颖的方法包含了...

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来源期刊

Connection Science 工程技术-计算机：理论方法

CiteScore

6.50

自引率

39.60%

发文量

审稿时长

3 months

期刊介绍： Connection Science is an interdisciplinary journal dedicated to exploring the convergence of the analytic and synthetic sciences, including neuroscience, computational modelling, artificial intelligence, machine learning, deep learning, Database, Big Data, quantum computing, Blockchain, Zero-Knowledge, Internet of Things, Cybersecurity, and parallel and distributed computing. A strong focus is on the articles arising from connectionist, probabilistic, dynamical, or evolutionary approaches in aspects of Computer Science, applied applications, and systems-level computational subjects that seek to understand models in science and engineering.