Performance Evaluation of Segment Anything Model with Variational Prompting for Application to Non-Visible Spectrum Imagery

• URL: http://arxiv.org/abs/2404.12285v1
• Date: Thu, 18 Apr 2024 16:04:14 GMT
• Title: Performance Evaluation of Segment Anything Model with Variational Prompting for Application to Non-Visible Spectrum Imagery
• Authors: Yona Falinie A. Gaus, Neelanjan Bhowmik, Brian K. S. Isaac-Medina, Toby P. Breckon,
• Abstract summary: 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.
• Score: 15.748043194987075
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Segment Anything Model (SAM) is a deep neural network foundational model designed to perform instance segmentation which has gained significant popularity given its zero-shot segmentation ability. SAM operates by generating masks based on various input prompts such as text, bounding boxes, points, or masks, introducing a novel methodology to overcome the constraints posed by dataset-specific scarcity. While SAM is trained on an extensive dataset, comprising ~11M images, it mostly consists of natural photographic images with only very limited images from other modalities. Whilst the rapid progress in visual infrared surveillance and X-ray security screening imaging technologies, driven forward by advances in deep learning, has significantly enhanced the ability to detect, classify and segment objects with high accuracy, it is not evident if the SAM zero-shot capabilities can be transferred to such modalities. This work assesses SAM capabilities in segmenting objects of interest in the X-ray/infrared modalities. Our approach reuses the pre-trained SAM with three different prompts: bounding box, centroid and random points. We present quantitative/qualitative results to showcase the performance on selected datasets. 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. Specifically, SAM performs poorly in segmenting slender objects and organic materials, such as plastic bottles. We find that infrared objects are also challenging to segment with point prompts given the low-contrast nature of this modality. This study shows that while SAM demonstrates outstanding zero-shot capabilities with box prompts, its performance ranges from moderate to poor for point prompts, indicating that special consideration on the cross-modal generalisation of SAM is needed when considering use on X-ray/infrared imagery.

Related papers

• Adapting Segment Anything Model for Unseen Object Instance Segmentation [70.60171342436092]
  Unseen Object Instance (UOIS) is crucial for autonomous robots operating in unstructured environments. We propose UOIS-SAM, a data-efficient solution for the UOIS task. UOIS-SAM integrates two key components: (i) a Heatmap-based Prompt Generator (HPG) to generate class-agnostic point prompts with precise foreground prediction, and (ii) a Hierarchical Discrimination Network (HDNet) that adapts SAM's mask decoder.
  arXiv  Detail & Related papers  (2024-09-23T19:05:50Z)
• IRSAM: Advancing Segment Anything Model for Infrared Small Target Detection [55.554484379021524]
  Infrared Small Target Detection (IRSTD) task falls short in achieving satisfying performance due to a notable domain gap between natural and infrared images. We propose the IRSAM model for IRSTD, which improves SAM's encoder-decoder architecture to learn better feature representation of infrared small objects.
  arXiv  Detail & Related papers  (2024-07-10T10:17:57Z)
• 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)
• 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)
• Stable Segment Anything Model [79.9005670886038]
  The Segment Anything Model (SAM) achieves remarkable promptable segmentation given high-quality prompts. This paper presents the first comprehensive analysis on SAM's segmentation stability across a diverse spectrum of prompt qualities. Our solution, termed Stable-SAM, offers several advantages: 1) improved SAM's segmentation stability across a wide range of prompt qualities, while 2) retaining SAM's powerful promptable segmentation efficiency and generality.
  arXiv  Detail & Related papers  (2023-11-27T12:51:42Z)
• Zero-Shot Segmentation of Eye Features Using the Segment Anything Model (SAM) [8.529233820032678]
  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.
  arXiv  Detail & Related papers  (2023-11-14T11:05:08Z)
• MA-SAM: Modality-agnostic SAM Adaptation for 3D Medical Image Segmentation [58.53672866662472]
  We introduce a modality-agnostic SAM adaptation framework, named as MA-SAM. Our method roots in the parameter-efficient fine-tuning strategy to update only a small portion of weight increments. By injecting a series of 3D adapters into the transformer blocks of the image encoder, our method enables the pre-trained 2D backbone to extract third-dimensional information from input data.
  arXiv  Detail & Related papers  (2023-09-16T02:41:53Z)
• SAM Meets Robotic Surgery: An Empirical Study in Robustness Perspective [21.2080716792596]
  Segment Anything Model (SAM) is a foundation model for semantic segmentation. We investigate the robustness and zero-shot generalizability of the SAM in the domain of robotic surgery.
  arXiv  Detail & Related papers  (2023-04-28T08:06:33Z)
• 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)
• Segment Anything Model for Medical Image Analysis: an Experimental Study [19.95972201734614]
  Segment Anything Model (SAM) is a foundation model that is intended to segment user-defined objects of interest in an interactive manner. We evaluate SAM's ability to segment medical images on a collection of 19 medical imaging datasets from various modalities and anatomies.
  arXiv  Detail & Related papers  (2023-04-20T17:50:18Z)
• 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.