MDS-DETR introduces a masked duplicate suppressor in self-attention to enable one-to-many supervision inside a single decoder, yielding +2.8 mAP over Deformable-DETR on COCO with 5% more training time and outperforming MR.DETR by 0.3 mAP while training 20% faster.
Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection
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UNVERDICTED 2representative citing papers
HiProto uses hierarchical prototypes with RPC-Loss, PR-Loss, and SPLGS to deliver competitive, interpretable object detection on low-quality datasets like ExDark and RTTS.
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MDS-DETR: DETR with Masked Duplicate Suppressor
MDS-DETR introduces a masked duplicate suppressor in self-attention to enable one-to-many supervision inside a single decoder, yielding +2.8 mAP over Deformable-DETR on COCO with 5% more training time and outperforming MR.DETR by 0.3 mAP while training 20% faster.
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HiProto: Hierarchical Prototype Learning for Interpretable Object Detection Under Low-quality Conditions
HiProto uses hierarchical prototypes with RPC-Loss, PR-Loss, and SPLGS to deliver competitive, interpretable object detection on low-quality datasets like ExDark and RTTS.