Segment Anything Is Not Always Perfect: An Investigation of SAM on Different Real-world Applications

Abstract 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. In this study, we 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. We analyze and discuss the benefits and limitations of SAM, while also presenting an outlook on its future development in segmentation tasks. By doing so, we aim to give a comprehensive…

• M
  Mitacs
• NS
  Natural Sciences and Engineering Research Council of Canada