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

• URL: http://arxiv.org/abs/2304.05750v3
• Date: Mon, 22 May 2023 06:22:40 GMT
• Title: Segment Anything Is Not Always Perfect: An Investigation of SAM on Different Real-world Applications
• Authors: Wei Ji, Jingjing Li, Qi Bi, Tingwei Liu, Wenbo Li, Li Cheng
• Abstract summary: Recently, Meta AI Research approaches a general, promptable Segment Anything Model (SAM) pre-trained on an unprecedentedly large segmentation dataset (SA-1B) 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.
• Score: 31.31905890353516
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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.

Related papers

• AlignSAM: Aligning Segment Anything Model to Open Context via Reinforcement Learning [61.666973416903005]
  Segment Anything Model (SAM) has demonstrated its impressive generalization capabilities in open-world scenarios with the guidance of prompts. We propose a novel framework, termed AlignSAM, designed for automatic prompting for aligning SAM to an open context.
  arXiv  Detail & Related papers  (2024-06-01T16:21:39Z)
• MAS-SAM: Segment Any Marine Animal with Aggregated Features [55.91291540810978]
  We propose a novel feature learning framework named MAS-SAM for marine animal segmentation. Our method enables to extract richer marine information from global contextual cues to fine-grained local details.
  arXiv  Detail & Related papers  (2024-04-24T07:38:14Z)
• Moving Object Segmentation: All You Need Is SAM (and Flow) [82.78026782967959]
  We investigate two models for combining SAM with optical flow that harness the segmentation power of SAM with the ability of flow to discover and group moving objects. In the first model, we adapt SAM to take optical flow, rather than RGB, as an input. In the second, SAM takes RGB as an input, and flow is used as a segmentation prompt. These surprisingly simple methods, without any further modifications, outperform all previous approaches by a considerable margin in both single and multi-object benchmarks.
  arXiv  Detail & Related papers  (2024-04-18T17:59:53Z)
• RAP-SAM: Towards Real-Time All-Purpose Segment Anything [120.17175256421622]
  Segment Anything Model (SAM) is one remarkable model that can achieve generalized segmentation. Current real-time segmentation mainly has one purpose, such as semantic segmentation on the driving scene. This work explores a new real-time segmentation setting, named all-purpose segmentation in real-time, to transfer VFMs in real-time deployment.
  arXiv  Detail & Related papers  (2024-01-18T18:59:30Z)
• Learning to Prompt Segment Anything Models [55.805816693815835]
  Segment Anything Models (SAMs) have demonstrated great potential in learning to segment anything. SAMs work with two types of prompts including spatial prompts (e.g., points) and semantic prompts (e.g., texts) We propose spatial-semantic prompt learning (SSPrompt) that learns effective semantic and spatial prompts for better SAMs.
  arXiv  Detail & Related papers  (2024-01-09T16:24:25Z)
• Boosting Segment Anything Model Towards Open-Vocabulary Learning [69.42565443181017]
  Segment Anything Model (SAM) has emerged as a new paradigmatic vision foundation model. Despite SAM finding applications and adaptations in various domains, its primary limitation lies in the inability to grasp object semantics. We present Sambor to seamlessly integrate SAM with the open-vocabulary object detector in an end-to-end framework.
  arXiv  Detail & Related papers  (2023-12-06T17:19:00Z)
• A Comprehensive Survey on Segment Anything Model for Vision and Beyond [7.920790211915402]
  It is urgent to design a general class of models, which we term foundation models, trained on broad data. The recently proposed segment anything model (SAM) has made significant progress in breaking the boundaries of segmentation. This paper introduces the background and terminology for foundation models including SAM, as well as state-of-the-art methods contemporaneous with SAM.
  arXiv  Detail & Related papers  (2023-05-14T16:23:22Z)
• A Survey on Segment Anything Model (SAM): Vision Foundation Model Meets Prompt Engineering [37.68799208121957]
  Segment anything model (SAM) developed by Meta AI Research has attracted significant attention. With the relevant papers and projects increasing exponentially, it is challenging for the readers to catch up with the development of SAM. This work conducts the first yet comprehensive survey on SAM.
  arXiv  Detail & Related papers  (2023-05-12T07:21:59Z)
• An Alternative to WSSS? An Empirical Study of the Segment Anything Model (SAM) on Weakly-Supervised Semantic Segmentation Problems [35.547433613976104]
  The Segment Anything Model (SAM) has demonstrated exceptional performance and versatility. This report explores the application of SAM in Weakly-Supervised Semantic (WSSS) We adapt SAM as the pseudo-label generation pipeline given only the image-level class labels.
  arXiv  Detail & Related papers  (2023-05-02T16:35:19Z)
• Segment anything, from space? [8.126645790463266]
  "Segment Anything Model" (SAM) can segment objects in input imagery based on cheap input prompts. SAM usually achieved recognition accuracy similar to, or sometimes exceeding, vision models that had been trained on the target tasks. We examine whether SAM's performance extends to overhead imagery problems and help guide the community's response to its development.
  arXiv  Detail & Related papers  (2023-04-25T17:14:36Z)
• SAM Fails to Segment Anything? -- SAM-Adapter: Adapting SAM in Underperformed Scenes: Camouflage, Shadow, Medical Image Segmentation, and More [13.047310918166762]
  We propose textbfSAM-Adapter, which incorporates domain-specific information or visual prompts into the segmentation network by using simple yet effective adapters. We can even outperform task-specific network models and achieve state-of-the-art performance in the task we tested: camouflaged object detection.
  arXiv  Detail & Related papers  (2023-04-18T17:38:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.