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Segment Anything Is Not Always Perfect: An Investigation of SAM on Different Real-world Applications
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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.
Forward citations
Cited by 2 Pith papers
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Segment Concealed Objects with Incomplete Supervision
SEE is a unified mean-teacher framework that derives SAM prompts from coarse teacher masks to generate pseudo-labels, and reports state-of-the-art results for weakly and semi-supervised concealed object segmentation.
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TextureSAM: Towards a Texture Aware Foundation Model for Segmentation
Fine-tuning SAM-2 on texture-augmented ADE20K shifts segmentation toward texture-defined boundaries, raising un-aggregated mIoU on natural and synthetic texture benchmarks.
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