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

Revisiting Contrastive Methods for Unsupervised Learning of Visual Representations

NeurIPS 2021 · Detection and segmentation › Open-set and few-shot detection · approach: Object-centric learning theory and video · 34 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
pretrain on scene-centric COCO instead of object-centric ImageNet-118KMoCo · VOC linear classificationmAP: 75.8 -> 80.9Helped
pretrain on COCO instead of ImageNet-118KMoCo · CIFAR10 linear classificationaccuracy: 83.1 -> 77.4Hurt
pretrain on COCO instead of IN-118K, then finetuneMoCo · NYUD semantic seg.mIoU: 37.7 -> 39.3Helped
pretrain on long-tailed ImageNet subset instead of uniformMoCo · VOC linear classificationmAP: 75.8 -> 76.1No clear effect
add multi-crop (ablation)MoCo · VOC classificationmAP: 76.1 -> 81.3Helped
constrain multi-crop to be within the same region (ablation)MoCo + MC · VOC classificationmAP: 81.3 -> 82.1Helped
lower momentum (ablation)MoCo + MC + CC · VOC classificationmAP: 82.1 -> 82.8Helped
stronger augmentations (ablation)MoCo + MC + CC + lower m · VOC classificationmAP: 82.8 -> 83.7Helped
add nearest neighbors positive loss (ablation)MoCo + MC + CC + lower m + A+ · VOC classificationmAP: 83.7 -> 85.1Helped
increase neighbors k from 1 to 50 (ablation)kNN-MoCo · VOC linearmAP: 83.6 -> 84.3Helped
raise neighbor loss weight lambda from 0.4 to 0.8 (ablation)kNN-MoCo · VOC linearmAP: 84.2 -> 80.7Hurt
mine neighbors before the projection head (ablation)kNN-MoCo · VOC linearmAP: 82.8 -> 84.2Helped

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