Instance Boundary Aware Segmentation

via “instance-boundary-aware-segmentation”

image-segmentation model by undefined. 1,02,623 downloads.

Unique: Uses learnable instance queries that are decoded through cross-attention to produce per-instance mask logits. Unlike Mask R-CNN (which requires bounding box proposals), OneFormer generates instance masks directly from queries without region proposals, enabling end-to-end instance segmentation.

vs others: Achieves 35.3 AP on ADE20K instance segmentation, comparable to Mask2Former (35.1 AP) while using fewer parameters. Faster than Mask R-CNN variants due to query-based approach, but may struggle with dense scenes (>100 instances) where proposal-based methods can be more selective.