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Research map / Detection and segmentation / Open-set and few-shot detection

Multi-Granularity Alignment Domain Adaptation for Object Detection

CVPR 2022 · Detection and segmentation › Open-set and few-shot detection · approach: Domain-adaptive object detection · 117 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
use MGADA instead of CDGFaster R-CNN with VGG-16 · Cityscapes to FoggyCityscapesmAP: 42.3 -> 44.3Helped
use MGADA instead of CFAFCOS with ResNet-101 · Cityscapes to FoggyCityscapesmAP: 40.2 -> 43.8Helped
use MGADA instead of CFAFCOS with VGG-16 · Sim10k to CityscapesmAP (car): 49.0 -> 54.6Helped
use MGADA instead of CFAFCOS with ResNet-101 · KITTI to CityscapesmAP (car): 45.0 -> 46.5Helped
use MGADA instead of UMTFaster R-CNN with ResNet-101 · PASCAL VOC to ClipartmAP: 44.1 -> 44.8Helped
use MGADA instead of UMTFaster R-CNN with ResNet-101 · PASCAL VOC to WatercolormAP: 58.1 -> 58.1No clear effect
add the category-level discriminator (ablation)MGADA · Cityscapes to FoggyCityscapesmAP: 39.3 -> 43.6Helped
add omni-scale gated fusion (ablation)MGADA · Cityscapes to FoggyCityscapesmAP: 41.3 -> 43.6Helped
replace average fusion with omni-scale gated fusion (ablation)MGADA · Cityscapes to FoggyCityscapesmAP: 42.1 -> 43.6Helped
replace 1x1 convolution fusion with omni-scale gated fusion (ablation)MGADA · Cityscapes to FoggyCityscapesmAP: 41.5 -> 43.6Helped
replace the baseline discriminator with a center-aware discriminator (ablation)MGADA discriminator comparison · Cityscapes to FoggyCityscapesmAP: 39.3 -> 40.5Helped
replace the baseline discriminator with a group discriminator (ablation)MGADA discriminator comparison · Cityscapes to FoggyCityscapesmAP: 39.3 -> 40.7Helped

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