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Federated Incremental Semantic Segmentation

CVPR 2023 · Continual learning › Class-incremental · approach: Unknown-class mining for incremental segmentation · 58 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use FBL instead of RCIL + FLFederated incremental semantic segmentation · Pascal-VOC 2012, 15-1 overlappedmIoU: 50.0 -> 56.6Helped
Use FBL instead of MiB + FLFederated incremental semantic segmentation · Pascal-VOC 2012, 4-4 overlappedmIoU: 33.0 -> 43.9Helped
Use FBL instead of RCIL + FLFederated incremental semantic segmentation · Pascal-VOC 2012, 8-2 overlappedmIoU: 33.7 -> 35.7Helped
Use FBL instead of RCIL + FLFederated incremental semantic segmentation · ADE20k, 100-10 overlappedmIoU: 26.4 -> 27.9Helped
Add adaptive class-balanced pseudo labeling using class-specific entropy thresholds (ablation)FBL · Pascal-VOC 2012, 4-4 overlappedmIoU: 40.0 -> 43.9Helped
Add adaptive class-balanced pseudo labeling using class-specific entropy thresholds (ablation)FBL · Pascal-VOC 2012, 8-2 overlappedmIoU: 26.7 -> 35.7Helped
Add forgetting-balanced semantic compensation loss (ablation)FBL · Pascal-VOC 2012, 4-4 overlappedmIoU: 40.0 -> 43.9Helped
Add forgetting-balanced semantic compensation loss (ablation)FBL · Pascal-VOC 2012, 8-2 overlappedmIoU: 29.2 -> 35.7Helped
Add forgetting-balanced relation consistency loss (ablation)FBL · Pascal-VOC 2012, 4-4 overlappedmIoU: 32.3 -> 43.9Helped
Add forgetting-balanced relation consistency loss (ablation)FBL · Pascal-VOC 2012, 8-2 overlappedmIoU: 28.7 -> 35.7Helped

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