AI BriefingUniversal Segmentation Based on Mask2Former
Key point
Introducing a universal model that performs instance, semantic, and panoptic segmentation in an integrated way through Mask2Former and OneFormer.
Details
This explains the concepts and differences among the three core tasks of image segmentation: Instance, Semantic, and Panoptic segmentation.
- Instance Segmentation: Identifies individual objects and generates binary masks.
- Semantic Segmentation: Classifies classes at the pixel level, distinguishing background (Stuff) and objects (Things).
- Panoptic Segmentation: Integrates instance and semantic segmentation to identify all segments.
In the past, separate architectures were required for each task, but with the emergence of Mask2Former and OneFormer, Universal Image Segmentation, which performs all tasks with a single architecture, has become possible. These models are implemented through the Hugging Face transformers library.
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