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

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arxiv 2304.05750 v3 pith:SIFKLQBS submitted 2023-04-12 cs.CV

Wei Ji , Jingjing Li , Qi Bi , Tingwei Liu , Wenbo Li , Li Cheng This is my paper · ORCID
classification cs.CV
keywords applicationssegmentationanythingbenefitsfuturepracticalresearchsegment
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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.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Segment Concealed Objects with Incomplete Supervision

    cs.CV 2025-06 conditional novelty 6.0 of 10

    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.

  2. TextureSAM: Towards a Texture Aware Foundation Model for Segmentation

    cs.CV 2025-05 conditional novelty 5.0 of 10

    Fine-tuning SAM-2 on texture-augmented ADE20K shifts segmentation toward texture-defined boundaries, raising un-aggregated mIoU on natural and synthetic texture benchmarks.

  3. INT: Instance-Specific Negative Mining for Task-Generic Promptable Segmentation

    cs.CV 2025-01 conditional novelty 5.0 of 10

    INT improves task-generic promptable segmentation by progressively mining negative candidates, using VLM output differences after masking to select and refine instance-specific prompts.

  4. Open Foundation Models in Healthcare: Challenges, Paradoxes, and Opportunities with GenAI Driven Personalized Prescription

    cs.CL 2025-02 conditional novelty 4.0 of 10

    Open LLMs (LLaMA-2, LLaMA-3, Mistral, Meditron) roughly match GPT-4 on a 25-patient prescription-suitability check when given SmPC context via RAG, though some interaction classes degrade with RAG.

  5. Self-Prompt SAM: Medical Image Segmentation via Automatic Prompt SAM Adaptation

    cs.CV 2025-02 conditional novelty 3.0 of 10

    Self-Prompt-SAM automatically generates point, box, and mask prompts for a fine-tuned SAM and reports state-of-the-art Dice scores on three medical segmentation benchmarks.

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