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

arXiv:2404.12285v1 Announce Type: new
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 …

abstract application arxiv cs.cv deep neural network evaluation foundational foundational model instance masks network neural network performance prompting prompts sam segment segment anything segment anything model segmentation spectrum text type zero-shot