An Alternative to WSSS? An Empirical Study of the Segment Anything Model (SAM) on Weakly-Supervised Semantic Segmentation Problems

• Published in: arXiv.org
• Main Authors: Sun, Weixuan, Liu, Zheyuan, Zhang, Yanhao, Zhong, Yiran, Barnes, Nick
• Format: Paper
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 18.06.2023
• Subjects: Image segmentation; Performance evaluation; Semantic segmentation

The Segment Anything Model (SAM) has demonstrated exceptional performance and versatility, making it a promising tool for various related tasks. In this report, we explore the application of SAM in Weakly-Supervised Semantic Segmentation (WSSS). Particularly, we adapt SAM as the pseudo-label generat...