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

Aligning Pretraining for Detection via Object-Level Contrastive Learning

NeurIPS 2021 · Detection and segmentation › Open-set and few-shot detection · approach: Object-level representation learning · 53 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add selective-search object proposals for object-level contrastive learning (ablation)SoCo · COCO 1x R50-FPNAP^bb: 38.1 -> 40.6Helped
add FPN to pretraining without head (ablation)SoCo · COCO 1x R50-FPNAP^bb: 40.6 -> 40.2Hurt
add R-CNN head along with FPN to pretraining (ablation)SoCo · COCO 1x R50-FPNAP^bb: 40.2 -> 41.2Helped
add scale-aware assignment (ablation)SoCo · COCO 1x R50-FPNAP^bb: 41.2 -> 41.6Helped
add box jitter (ablation)SoCo · COCO 1x R50-FPNAP^bb: 41.6 -> 41.7No clear effect
add multi-view V3 (scale augmentation) (ablation)SoCo · COCO 1x R50-FPNAP^bb: 41.7 -> 42.3Helped
use selective search instead of random boxes (K=1) (ablation)SoCo · COCO 1x R50-FPNAP^bb: 41.4 -> 41.6Helped
vary proposals per image K from 4 (ablation)SoCo · COCO 1x R50-FPNAP^bb: 42.3 -> 41.6 (K=1), 41.2 (K=16)Hurt
lower EMA momentum from 0.99 to 0.98 (ablation)SoCo · COCO 1x R50-FPNAP^bb: 42.3 -> 35.0Hurt
increase batch size from 2048 to 4096 (ablation)SoCo · COCO 1x R50-FPNAP^bb: 42.3 -> 41.4Hurt
vary V3 resolution (96 to 192) (ablation)SoCo · COCO 1x R50-FPNAP^bb: 42.0-42.3No clear effect
add extra view V4 (SoCo*)SoCo 400ep · COCO 1x R50-FPNAP^bb: 43.0 -> 43.2Helped

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