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

Exploring Object Relation in Mean Teacher for Cross-Domain Detection

CVPR 2019 · Detection and segmentation › Open-set and few-shot detection · approach: Cross-domain translation and pose · 357 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add region-level prediction consistency to source-only detection (ablation)Faster R-CNN cross-domain detection · Foggy Cityscapes valmAP: 26.9 -> 34.2Helped
Add inter-graph consistency to region-level consistency (ablation)MTOR_R · Foggy Cityscapes valmAP: 34.2 -> 34.5Helped
Add intra-graph consistency to region-level consistency (ablation)MTOR_R · Foggy Cityscapes valmAP: 34.2 -> 34.9Helped
Add both inter-graph and intra-graph consistency (ablation)MTOR_R · Foggy Cityscapes valmAP: 34.2 -> 35.1Helped
Add inter-graph consistency to region-level consistency (ablation)MTOR_R · Cityscapes val (SIM10k → Cityscapes)car AP: 45.9 -> 46.1Helped
Add intra-graph consistency to region-level consistency (ablation)MTOR_R · Cityscapes val (SIM10k → Cityscapes)car AP: 45.9 -> 46.3Helped
Add both inter-graph and intra-graph consistency (ablation)MTOR_R · Cityscapes val (SIM10k → Cityscapes)car AP: 45.9 -> 46.6Helped
Add inter-graph consistency to region-level consistency (ablation)MTOR_R · COCO val (Syn2Real synthetic → real)mAP: 19.3 -> 19.6Helped
Add intra-graph consistency to region-level consistency (ablation)MTOR_R · COCO val (Syn2Real synthetic → real)mAP: 19.3 -> 20.0Helped
Add both inter-graph and intra-graph consistency (ablation)MTOR_R · COCO val (Syn2Real synthetic → real)mAP: 19.3 -> 20.7Helped
Tune consistency-loss tradeoff weight and teacher EMA smoothing coefficient (ablation)MTOR · Cityscapes → Foggy CityscapesmAPMixed

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