Discrete Image Tokenization For Unified Sequence Representation

via “semantic segmentation mask generation”

Microsoft's unified model for diverse vision tasks.

Unique: Represents segmentation masks as coordinate sequences in text format rather than dense feature maps, enabling variable-resolution output and mask complexity through the same seq2seq decoder used for detection and captioning

vs others: Unified model eliminates segmentation-specific infrastructure but with 10-15% lower mIoU than Mask R-CNN or DeepLab on standard benchmarks due to sequence-based representation constraints