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

Objaverse: A Universe of Annotated 3D Objects

CVPR 2023 · Detection and segmentation › Open-set and few-shot detection · approach: Open-vocabulary 3D and world perception · 698 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add Objaverse 3D copy-paste augmentation to GOLMask-RCNN ResNet-50 on LVIS · LVISAP: 27.7 -> 28.3Helped
Add Objaverse 3D copy-paste augmentation to GOLMask-RCNN ResNet-50 on LVIS · LVIS rare categoriesAPr: 21.4 -> 21.8Helped
Add Objaverse 3D copy-paste augmentation to GOLMask-RCNN ResNet-50 on LVIS · LVIS common categoriesAPc: 27.7 -> 28.3Helped
Add Objaverse 3D copy-paste augmentation to GOLMask-RCNN ResNet-50 on LVIS · LVIS frequent categoriesAPf: 30.4 -> 31.1Helped
Add Objaverse 3D copy-paste augmentation to GOLMask-RCNN ResNet-50 on LVIS detection · LVIS detectionAP: 27.5 -> 28.9Helped
Add Objaverse 3D copy-paste augmentation to GOLMask-RCNN ResNet-50 on LVIS detection · LVIS rare-category detectionAPr: 19.8 -> 21.8Helped
Train GET3D on Objaverse bags rather than ShapeNet bagsGET3D · Bag generation diversityhuman preference for greater diversity: 91%Helped
Increase ObjectNav training from 18 million to approximately 460 million simulation stepsObjaverse-trained ObjectNav agent · Open-Vocabulary ObjectNavsuccess rate: 19.9% -> 33.0%Helped
Use an Objaverse-trained ObjectNav agent instead of a random policyOpen-Vocabulary ObjectNav · Open-Vocabulary ObjectNavsuccess rate: 5.1% -> 19.9%Helped
Evaluate CLIP on random object orientations rather than the best-performing orientationLAION-2B ViT-L/14 · Rotational robustness benchmarkTop-1 accuracy: 52.1% -> 32.9%Hurt

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