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Research map / Detection and segmentation / CNN features

Correlation Loss: Enforcing Correlation between Classification and Localization

AAAI 2023 · Detection and segmentation › CNN features · approach: Box regression and loss design · 7 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add Correlation Loss to Sparse R-CNN using Spearman correlationSparse R-CNN · COCO minivalAP_C: 37.7 -> 39.3Helped
Add Correlation Loss to Sparse R-CNN using Spearman correlationSparse R-CNN · CityscapesAP: 39.0 -> 40.8Helped
Add Concordance Correlation LossATSS · COCO minivalAP_C: 38.7 -> 39.8Helped
Add Concordance Correlation LossFoveaBox · COCO minivalAP_C: 36.4 -> 37.1Helped
Add Spearman Correlation LossRetinaNet · COCO minivalAP_C: 36.5 -> 37.5Helped
Add Spearman Correlation LossYOLACT · COCO instance segmentationAP_C^mask: 28.3 -> 29.0Helped
Combine Correlation Loss with Rank & Sort LossATSS · COCO minivalAP_C: 39.9 -> 40.2Helped
Use Correlation Loss without an auxiliary head rather than adding an auxiliary headATSS · COCO minivalAP_C: 39.3 vs 39.8Helped
Backpropagate Correlation Loss through only the localization head instead of only the classification head (ablation)Sparse R-CNN · COCO minivalAP: 39.3 -> 37.5Hurt
Backpropagate Correlation Loss through both heads instead of only the classification head (ablation)Sparse R-CNN · COCO minivalAP: 39.3 -> 38.9Hurt
Use Pearson correlation instead of Concordance correlation (ablation)ATSS · COCO minivalAP_C: 39.8 -> 39.4Hurt
Use Pearson correlation instead of Spearman correlation (ablation)Sparse R-CNN · COCO minivalAP_75: 42.5 -> 42.2Hurt

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