Segment Anything Is Not Always Perfect: An Investigation of SAM on Different Real-world Applications
This work offers insights for researchers and practitioners using SAM in various domains, but it is incremental as it evaluates an existing model without introducing new methods.
The paper investigates the performance of the Segment Anything Model (SAM) across real-world applications like natural images, agriculture, manufacturing, remote sensing, and healthcare, analyzing its benefits and limitations to provide a comprehensive understanding.
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 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.