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SARAS-Net: Scale and Relation Aware Siamese Network for Change Detection

AAAI 2023 · Detection and segmentation › CNN features · approach: Domain-specific segmentation and detection · 77 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use SARAS-Net with ResNet50 instead of the strongest listed baselineLEVIR-CD change detection · LEVIR-CDF1: 90.99 -> 91.91Helped
Use SARAS-Net with ResNet50 instead of the strongest listed baselineWHU-CD change detection · WHU-CDF1: 89.72 -> 90.99Helped
Use SARAS-Net with ResNet50 instead of the strongest listed baselineDSIFN-CD change detection · DSIFN-CDF1: 62.95 -> 67.58Helped
Add the relation-aware module (ablation)SARAS-Net with SA and CT · LEVIR-CDF1: 91.26 -> 91.91Helped
Add the scale-aware module (ablation)SARAS-Net with RA and CT · LEVIR-CDF1: 91.11 -> 91.91Helped
Add the cross-transformer module (ablation)SARAS-Net with RA and SA · LEVIR-CDF1: 90.92 -> 91.91Helped
Add the cross-self-attention mechanism to cross-attention (ablation)SARAS-Net relation-aware module · LEVIR-CDF1: 91.54 -> 91.91Helped
Replace cross-self-attention with self-attention (ablation)SARAS-Net relation-aware module · LEVIR-CDF1: 91.91 -> 90.56Hurt

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