Zero-Shot Segmentation of Eye Features Using the Segment Anything Model (SAM)

• URL: http://arxiv.org/abs/2311.08077v2
• Date: Mon, 8 Apr 2024 13:23:47 GMT
• Title: Zero-Shot Segmentation of Eye Features Using the Segment Anything Model (SAM)
• Authors: Virmarie Maquiling, Sean Anthony Byrne, Diederick C. Niehorster, Marcus Nyström, Enkelejda Kasneci,
• Abstract summary: The Segment Anything Model (SAM) is the first foundation model for image segmentation. In this study, we evaluate SAM's ability to segment features from eye images recorded in virtual reality setups. Our investigation centers on SAM's zero-shot learning abilities and the effectiveness of prompts like bounding boxes or point clicks.
• Score: 8.529233820032678
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: The advent of foundation models signals a new era in artificial intelligence. The Segment Anything Model (SAM) is the first foundation model for image segmentation. In this study, we evaluate SAM's ability to segment features from eye images recorded in virtual reality setups. The increasing requirement for annotated eye-image datasets presents a significant opportunity for SAM to redefine the landscape of data annotation in gaze estimation. Our investigation centers on SAM's zero-shot learning abilities and the effectiveness of prompts like bounding boxes or point clicks. Our results are consistent with studies in other domains, demonstrating that SAM's segmentation effectiveness can be on-par with specialized models depending on the feature, with prompts improving its performance, evidenced by an IoU of 93.34% for pupil segmentation in one dataset. Foundation models like SAM could revolutionize gaze estimation by enabling quick and easy image segmentation, reducing reliance on specialized models and extensive manual annotation.

Related papers

• Segment Anything for Videos: A Systematic Survey [52.28931543292431]
  The recent wave of foundation models has witnessed tremendous success in computer vision (CV) and beyond. The segment anything model (SAM) has sparked a passion for exploring task-agnostic visual foundation models. This work conducts a systematic review on SAM for videos in the era of foundation models.
  arXiv  Detail & Related papers  (2024-07-31T02:24:53Z)
• ASAM: Boosting Segment Anything Model with Adversarial Tuning [9.566046692165884]
  This paper introduces ASAM, a novel methodology that amplifies a foundation model's performance through adversarial tuning. We harness the potential of natural adversarial examples, inspired by their successful implementation in natural language processing. Our approach maintains the photorealism of adversarial examples and ensures alignment with original mask annotations.
  arXiv  Detail & Related papers  (2024-05-01T00:13:05Z)
• 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)
• Performance Evaluation of Segment Anything Model with Variational Prompting for Application to Non-Visible Spectrum Imagery [15.748043194987075]
  This work assesses Segment Anything Model capabilities in segmenting objects of interest in the X-ray/infrared modalities. Our results show that SAM can segment objects in the X-ray modality when given a box prompt, but its performance varies for point prompts. We find that infrared objects are also challenging to segment with point prompts given the low-contrast nature of this modality.
  arXiv  Detail & Related papers  (2024-04-18T16:04:14Z)
• 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)
• Self-guided Few-shot Semantic Segmentation for Remote Sensing Imagery Based on Large Vision Models [14.292149307183967]
  This research introduces a structured framework designed for the automation of few-shot semantic segmentation. It utilizes the SAM model and facilitates a more efficient generation of semantically discernible segmentation outcomes. Central to our methodology is a novel automatic prompt learning approach, leveraging prior guided masks to produce coarse pixel-wise prompts for SAM.
  arXiv  Detail & Related papers  (2023-11-22T07:07:55Z)
• Semantic-SAM: Segment and Recognize Anything at Any Granularity [83.64686655044765]
  We introduce Semantic-SAM, a universal image segmentation model to enable segment and recognize anything at any desired granularity. We consolidate multiple datasets across three granularities and introduce decoupled classification for objects and parts. For the multi-granularity capability, we propose a multi-choice learning scheme during training, enabling each click to generate masks at multiple levels.
  arXiv  Detail & Related papers  (2023-07-10T17:59:40Z)
• RefSAM: Efficiently Adapting Segmenting Anything Model for Referring Video Object Segmentation [53.4319652364256]
  This paper presents the RefSAM model, which explores the potential of SAM for referring video object segmentation. Our proposed approach adapts the original SAM model to enhance cross-modality learning by employing a lightweight Cross-RValModal. We employ a parameter-efficient tuning strategy to align and fuse the language and vision features effectively.
  arXiv  Detail & Related papers  (2023-07-03T13:21:58Z)
• Personalize Segment Anything Model with One Shot [52.54453744941516]
  We propose a training-free Personalization approach for Segment Anything Model (SAM) Given only a single image with a reference mask, PerSAM first localizes the target concept by a location prior. PerSAM segments it within other images or videos via three techniques: target-guided attention, target-semantic prompting, and cascaded post-refinement.
  arXiv  Detail & Related papers  (2023-05-04T17:59:36Z)
• 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)
• Input Augmentation with SAM: Boosting Medical Image Segmentation with Segmentation Foundation Model [36.015065439244495]
  The Segment Anything Model (SAM) is a recently developed large model for general-purpose segmentation for computer vision tasks. SAM was trained using 11 million images with over 1 billion masks and can produce segmentation results for a wide range of objects in natural scene images. This paper shows that although SAM does not immediately give high-quality segmentation for medical image data, its generated masks, features, and stability scores are useful for building and training better medical image segmentation models.
  arXiv  Detail & Related papers  (2023-04-22T07:11:53Z)

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.