When SAM Meets Shadow Detection

• URL: http://arxiv.org/abs/2305.11513v1
• Date: Fri, 19 May 2023 08:26:08 GMT
• Title: When SAM Meets Shadow Detection
• Authors: Leiping Jie, Hui Zhang
• Abstract summary: We try segment anything model (SAM) on an unexplored popular task: shadow detection. Experiments show that the performance for shadow detection using SAM is not satisfactory, especially when comparing with the elaborate models.
• Score: 2.9324443830722973
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As a promptable generic object segmentation model, segment anything model (SAM) has recently attracted significant attention, and also demonstrates its powerful performance. Nevertheless, it still meets its Waterloo when encountering several tasks, e.g., medical image segmentation, camouflaged object detection, etc. In this report, we try SAM on an unexplored popular task: shadow detection. Specifically, four benchmarks were chosen and evaluated with widely used metrics. The experimental results show that the performance for shadow detection using SAM is not satisfactory, especially when comparing with the elaborate models. Code is available at https://github.com/LeipingJie/SAMSh.

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