OneFormer: One Transformer to Rule Universal Image Segmentation

Understand

Universal Image Segmentation is not a new concept.

• Past attempts to unify image segmentation in the last decades include scene parsing, panoptic segmentation, and, more recently, new panoptic architectures.
• However, such panoptic architectures do not truly unify image segmentation because they need to be trained individually on the semantic, instance, or panoptic segmentation to achieve the best performance.
• Ideally, a truly universal framework should be trained only once and achieve SOTA performance across all three image segmentation tasks.