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The Segment Anything Model (SAM) has demonstrated exceptional performance andversatility, making it a promising tool for various related tasks. In thisreport, we explore the application of SAM in Weakly-Supervised SemanticSegmentation (WSSS). Particularly, we adapt SAM as the pseudo-label generationpipeline given only the image-level class labels. While we observed impressiveresults in most cases, we also identify certain limitations. Our study includesperformance evaluations on PASCAL VOC and MS-COCO, where we achieved remarkableimprovements over the latest state-of-the-art methods on both datasets. Weanticipate that this report encourages further explorations of adopting SAM inWSSS, as well as wider real-world applications.

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