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Proposal-Contrastive Pretraining for Object Detection from Fewer Data

ICLR 2023 · Detection and segmentation › CNN features · approach: Region-based and query-based detectors · 0 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Localization-aware positives in contrastive loss (LocSCE, IoU threshold 0.5) vs original SCE (ablation)ProSeCo · Mini-COCO 5%mAP: +1.7Helped
Use a fixed backbone pretrained with local-info (SCRL) instead of global-info (SwAV) (ablation)ProSeCo · Mini-COCO 5%mAP: +1 p.p.Helped
Batch size 192 instead of 64 (ablation)ProSeCo · Mini-COCO 5%mAP: -0.2Hurt
Large-scale images in weak augmentation vs mid-scale (ablation)ProSeCo · Mini-COCO 5%mAP: +0.7Helped
Temperature tau_t 0.07 vs 0.05 (ablation)ProSeCo · Mini-COCO 5%mAP: +0.3Helped
EMA keep rate 0.999 vs 0.99/0.996 (ablation)ProSeCo · Mini-COCO 5%mAP: +0.1 over 0.996 is 1.0 difference; text reports +0.1 p.p. overallHelped
InfoNCE instead of SCE (no localization) (ablation)ProSeCo · Mini-COCO 5%mAP: +0.3Helped
Localization positives on InfoNCE (LocNCE) vs SCE-based LocSCE (ablation)ProSeCo · Mini-COCO 5%mAP: LocSCE +0.8 over LocNCEMixed
ProSeCo pretraining vs DETReg on Def. DETR across Mini-COCO, FSOD, and VOC settingsDef. DETR · Mini-COCO 1%/5%/10%, FSOD-test, Mini-VOC, PASCAL VOCmAP: +2.2 to +4.1 over DETRegHelped
ProSeCo pretraining on full COCO 1x finetuningDef. DETR · COCO full (118k)mAP: 0.0 vs DETRegNo clear effect
Pretraining on COCO instead of ImageNet (ablation)ProSeCo · Mini-COCO 5%mAP: -0.4Hurt

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