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. To that end, we propose OneFormer, a universal image segmentation framework that unifies segmentation with a multi-task train-once design. 2022: Jitesh Jain, Jiacheng Li, M. Chiu, Ali Hassani, Nikita Orlov, H. Shi Ranked #1 on Instance Segmentation on ADE20K val https://arxiv.org/pdf/2211.06220v1.pdf
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