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Class Similarity Weighted Knowledge Distillation for Continual Semantic Segmentation

CVPR 2022 · Continual learning › Class-incremental · approach: Distillation-based continual segmentation · 63 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use class-similarity-weighted knowledge distillation instead of normal knowledge distillation (ablation)REMINDER · Pascal-VOC 2012 15-1all mIoU (%): 23.69 -> 58.52Helped
Use class-similarity-weighted knowledge distillation instead of UNKD (ablation)REMINDER · Pascal-VOC 2012 15-1all mIoU (%): 50.29 -> 58.52Helped
Add CSW-KD to the output-distillation objective (ablation)REMINDER · Pascal-VOC 2012 15-1avg mIoU (%): 62.47 -> 68.27Helped
Use REMINDER rather than PLOPcontinual semantic segmentation · Pascal-VOC 2012 15-1all mIoU (%): 54.68 -> 58.52Helped
Use REMINDER rather than PLOPcontinual semantic segmentation · Pascal-VOC 2012 15-1new-class mIoU (%): 26.76 -> 27.23Helped
Use REMINDER rather than PLOPcontinual semantic segmentation · ADE20k 100-10all mIoU (%): 30.52 -> 33.11Helped
Use REMINDER rather than PLOPcontinual semantic segmentation · ADE20k 100-10avg mIoU (%): 34.48 -> 37.47Helped
Use REMINDER rather than PLOPcontinual semantic segmentation · ADE20k 50-50all mIoU (%): 29.41 -> 29.39No clear effect

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