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Segment anything is not always perfect: an investigation of SAM on different real-world applications - EurekAlert
Recently, Meta AI Research approaches a general, promptable segment anything model (SAM) pre-trained on an unprecedentedly large segmentation dataset (SA-1B). Without a doubt, the emergence of SAM will yield significant benefits for a wide array of practical image segmentation applications. Researchers from University of Alberta conduct a series of intriguing investigations into the performance of SAM across various applications, particularly in the fields of natural images, agriculture, manufacturing, remote sensing and healthcare. They analyze and discuss the benefits and limitations of SAM, while also presenting an outlook on its future development in segmentation tasks. By doing so, they aim to give a comprehensive understanding of SAM′s practical applications. This work is expected to provide insights that facilitate future research activities toward generic segmentation. Source code is publicly available at https://github.com/LiuTingWed/SAM-Not-Perfect.
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