Dual-Selective Network for Domain-Incremental Change Detection
Domain-incremental change detection (DICD) continuously adapts models to new geographic domains while preserving prior knowledge. However, a structural mismatch exists: the label space remains fixed while domain characteristics vary drastically. Consequently, incremental models struggle to maintain stable spatial change representations across domains. Existing strategies, such as replay-based or regularization-based methods, often fail to scale to long domain sequences, leading to knowledge degradation or increased computational cost. We propose Dual-Selective Incremental Network (DSINet), a u
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- LinkedLinked via arxiv author · 85%Yuzhi He →
“Dual-Selective Network for Domain-Incremental Change Detection”
- LinkedLinked via arxiv author · 85%Junxi Huang →
“Dual-Selective Network for Domain-Incremental Change Detection”
- LinkedLinked via arxiv author · 85%Haorui Wu →
“Dual-Selective Network for Domain-Incremental Change Detection”
- LinkedLinked via arxiv author · 85%Jiahui Qu →
“Dual-Selective Network for Domain-Incremental Change Detection”