Demystifying Segment Anything: A Comprehensive Guide to Next-Gen Image Segmentation

Foundation models, which are large-scale pre-trained models, have significantly impacted the field of natural language processing (NLP) with their zero-shot and few-shot generalization capabilities. Recently, these models have been applied to computer vision tasks, such as image-text alignment, using contrastive learning. However, there's a need to expand foundation models for a wider range of computer vision tasks, such as image segmentation. In this research paper, the authors propose a foundation model for image segmentation, which they call "Segment Anything." The researchers propose a promotable segmentation task, inspired by the prompting techniques used in NLP foundation models.