SmooSeg: smoothness prior for unsupervised semantic segmentation

• Main Authors: Lan, Mengcheng, Wang, Xinjiang, Ke, Yiping, Xu, Jiaxing, Feng, Litong, Zhang, Wayne
• Other Authors: School of Computer Science and Engineering
• Format: Conference or Workshop Item
• Language: English
• Published: 2024
• Subjects: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence; Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision; Unsupervised Semantic Segmentation; Smoothness Prior
• Online Access: https://hdl.handle.net/10356/173324; https://neurips.cc/
• Institution: Nanyang Technological University

Unsupervised semantic segmentation is a challenging task that segments images into semantic groups without manual annotation. Prior works have primarily focused on leveraging prior knowledge of semantic consistency or priori concepts from self-supervised learning methods, which often overlook the co...