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An End-To-End Network for Panoptic Segmentation

CVPR 2019 · Detection and segmentation › CNN features · approach: Unified panoptic segmentation · 148 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add the spatial ranking module (ablation)OANet with ResNet-50 · COCO Panoptic validationPQ: 37.2 -> 39.0Helped
add the spatial ranking module (ablation)OANet with ResNet-101 · COCO Panoptic validationPQ: 38.8 -> 40.7Helped
replace instance training labels with panoptic non-overlapping instance annotations (ablation)OANet with ResNet-50 · COCO Panoptic validationPQ: 37.2 -> 36.1Hurt
supervise the stuff branch on object classes as well as stuff classes (ablation)OANet with ResNet-50 · COCO Panoptic validationPQ: 36.7 -> 37.2Helped
share backbone features between the stuff and instance branches (ablation)OANet with ResNet-50 · COCO Panoptic validationPQ: 36.5 -> 37.0Helped
also share FPN skip-connection features (ablation)OANet with ResNet-50 · COCO Panoptic validationPQ: 37.0 -> 37.2Helped
share backbone and FPN skip-connection features instead of no sharing (ablation)OANet with ResNet-101 · COCO Panoptic validationPQ: 38.2 -> 38.8Helped
set loss-balance λ to 0.25 rather than 0.2 (ablation)OANet with ResNet-50 · COCO Panoptic validationPQ: 36.9 -> 37.2Helped
increase loss-balance λ from 0.25 to 0.33 (ablation)OANet with ResNet-50 · COCO Panoptic validationPQ: 37.2 -> 36.9Hurt
increase loss-balance λ from 0.25 to 0.50 (ablation)OANet with ResNet-50 · COCO Panoptic validationPQ: 37.2 -> 36.5Hurt
increase loss-balance λ from 0.25 to 0.75 (ablation)OANet with ResNet-50 · COCO Panoptic validationPQ: 37.2 -> 35.3Hurt
set loss-balance λ to 1.0 (ablation)OANet with ResNet-50 · COCO Panoptic validationPQHurt

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