MaskMerge: Structure-aware Token Merging for Efficient Instance Segmentation
Token merging improves the efficiency of Vision Transformers by combining redundant tokens, but existing methods largely rely on feature similarity to determine which tokens to merge. This criterion is effective for semantic segmentation, yet can fail in instance segmentation, where adjacent objects of the same class often have highly similar features. We identify this failure mode as cross-instance token fusion. We introduce MaskMerge, an instance-aware token merging method that uses coarse intermediate mask predictions to constrain feature-based merging and preserve instance boundaries. We evaluate MaskMerge on two diverse instance segmentation domains characterized by high inter-instan...
| Authors | Bavesh Balaji, Letian Jiang, Alexander Wong et al. |
|---|---|
| Primary category | cs.CV |
| Published | 16 October 2026 |
| Last updated at source | 16 October 2026 |
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