Self-supervised pretraining has been shown to yield powerful representations
for transfer learning. These performance gains come at a large computational
cost however, with state-of-the-art methods requiring an order of magnitude
more computation than supervised pretraining. We tackle this computational
bottleneck by introducing a new self-supervised objective, contrastive
detection, which tasks representations with identifying object-level features
across augmentations. This objective extracts a rich learning signal per image,
leading to state-of-the-art transfer accuracy on a variety of downstream tasks,
while requiring up to 10x less pretraining. In particular, our strongest
ImageNet-pretrained model performs on par with SEER, one of the largest
self-supervised systems to date, which uses 1000x more pretraining data.
Finally, our objective seamlessly handles pretraining on more complex images
such as those in COCO, closing the gap with supervised transfer learning from
COCO to PASCAL.

引入对比检测算法的自监督目标，通过在图像增强中识别物体水平特征提取富有学习信息的信号，实现高效的自监督预训练，拥有多种下游任务的领先转移精度。在 ImageNet 预训练模型表现与大型自监督系统 SEER 相当的同时，需要少至 10 倍的预训练数据量，最终该目标还能轻松应对更复杂的图像。