Segmentation

In my last post, we explored how Segment Anything (SAM) works in image segmentation, breaking down the key components of its model architecture. SAM achieved great success in image segmentation, demonstrating two key strengths: its foundation as a large-scale model trained on an extensive dataset and its ability to be promptable, allowing users to generate segmentations with flexible inputs. These two strengths allow SAM to deliver impressive performance in a zero-shot setting....

Segment Anything (SAM) has drawn massive attention in the computer vision community, accumulating an impressive 8,000 citations. Segmentation has long been a crucial yet challenging aspect of computer vision. One of the biggest hurdles? Annotation. Unlike simple bounding boxes, which only require marking the object’s general location, segmentation demands precise pixel-level annotations—an incredibly tedious and time-consuming task for annotators. SAM is one of the first large-scale foundation models for segmentation....