MILE: Mixture of Incremental LoRA Experts for Continual Semantic Segmentation across Domains and Modalities
Continual semantic segmentation requires models to adapt to new domains or modalities without sacrificing performance on previously learned tasks. Expert-based learning, in which task-specific modules specialize in different domains, has proven effective in mitigating forgetting. These methods include dynamic expansion, which suffers from scalability issues, or parameter isolation, which constrains the ability to learn new tasks. We introduce Mixture of Incremental LoRA Experts (MILE), a modular and parameter-efficient framework for continual segmentation across both domains and modalities. MILE leverages Low-Rank Adaptation (LoRA) to instantiate lightweight experts for each new task whil...
| Authors | Shishir Muralidhara, Didier Stricker, René Schuster |
|---|---|
| Primary category | cs.CV |
| Published | 5 May 2026 |
| Last updated at source | 7 August 2026 |
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