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MeGA-CDA: Memory Guided Attention for Category-Aware Unsupervised Domain Adaptive Object Detection

CVPR 2021 · Detection and segmentation › Open-set and few-shot detection · approach: Domain-adaptive object detection · 214 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add category-wise discriminators with cosine-similarity memory-guided attention at conv5 (ablation)Global domain adaptation (GDA) · Cityscapes → Foggy-CityscapesmAP: 36.2 -> 36.8Helped
Apply cosine-similarity category-wise adaptation at both conv4 and conv5 (ablation)GDA+CDA+MA at conv5 · Cityscapes → Foggy-CityscapesmAP: 36.8 -> 40.0Helped
Use learned similarity instead of cosine similarity for attention (ablation)GDA+CDA+MA at conv5 · Cityscapes → Foggy-CityscapesmAP: 36.8 -> 38.9Helped
Use learned similarity instead of cosine similarity with adaptation at conv4 and conv5 (ablation)GDA+CDA+MA at conv4 and conv5 · Cityscapes → Foggy-CityscapesmAP: 40.0 -> 41.8Helped
Use MeGA-CDA instead of the strongest reported baselineObject detector · Cityscapes → Foggy-CityscapesmAP: 39.3 -> 41.8Helped
Use MeGA-CDA instead of the strongest reported baselineObject detector · Sim10K → CityscapesmAP: 43.0 -> 44.8Helped
Use MeGA-CDA instead of the strongest reported baselineObject detector · KITTI → CityscapesmAP: 42.5 -> 43.0Helped
Use MeGA-CDA instead of the strongest reported baselineObject detector · Cityscapes → KITTImAP: 73.5 -> 75.5Helped

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