Transcription of DetCo: Unsupervised Contrastive Learning for Object Detection
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DetCo: Unsupervised Contrastive Learning for Object Detection
DetCo: Unsupervised Contrastive Learning for Object DetectionEnze Xie1 , Jian Ding3*, Wenhai Wang4, Xiaohang Zhan5,Hang Xu2, Peize Sun1, Zhenguo Li2, Ping Luo11 The University of Hong Kong2 Huawei Noah s Ark Lab3 Wuhan University4 Nanjing University5 Chinese University of Hong KongAbstractWe present DetCo, a simple yet effective self-supervisedapproach for Object Detection . Unsupervised pre-trainingmethods have been recently designed for Object Detection ,but they are usually deficient in image classification, or theopposite. Unlike them, DetCo transfers well on downstreaminstance-level dense prediction tasks, while maintainingcompetitive image-level classification accuracy. The advan-tages are derived from (1) multi-level supervision to inter-mediate representations, (2) Contrastive Learning betweenglobal image and local patches. These two designs facil-itate discriminative and consistent global and local repre-sentation at each level of feature pyramid, improving detec-tion and classification, experiments on VOC, COCO, Cityscapes, andImageNet demonstrate that DetCo not only outperforms re-cent methods on a series of 2D and 3D instance-level de-tection tasks, but also competitive on image example, on ImageNet classification, DetCo is top-1 accuracy better than InsLoc and DenseCL,which are two contemporary works designed for Object de-tection.
contrastive learning [5,19,5,3,18] currently achieved state-of-the-art performance, arousing extensive attention from researchers. Unlike generative methods, contrastive learning avoids the computation-consuming generation step by pulling representations of different views of the same im-age (i.e., positive pairs) close, and pushing representations
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