CASIA OpenIR  > 模式识别国家重点实验室  > 图像与视频分析
UniVIP: A Unified Framework for Self-Supervised Visual Pre-training
Li, Zhaowen1,2; Zhu, Yousong1; Yang, Fan3; Li, Wei3; Zhao, Chaoyang1,4; Chen, Yingying1; Chen, Zhiyang1,2; Xie, Jiahao5; Wu, Liwei3; Zhao, Rui3,7; Tang, Ming1; Wang, Jinqiao1,2,6
2022-06
Conference NameIEEE Computer Vision and Pattern Recognition (CVPR)
Conference Date2022-6-19
Conference PlaceNew Orleans, Louisiana & Online
Abstract

Self-supervised learning (SSL) holds promise in leveraging large amounts of unlabeled data. However, the success of popular SSL methods has limited on single-centricobject images like those in ImageNet and ignores the correlation among the scene and instances, as well as the semantic difference of instances in the scene. To address the
above problems, we propose a Unified Self-supervised Visual Pre-training (UniVIP), a novel self-supervised framework to learn versatile visual representations on either
single-centric-object or non-iconic dataset. The framework takes into account the representation learning at three
levels: 1) the similarity of scene-scene, 2) the correlation of scene-instance, 3) the discrimination of instanceinstance. During the learning, we adopt the optimal transport algorithm to automatically measure the discrimination of instances. Massive experiments show that UniVIP pre-trained on non-iconic COCO achieves state-ofthe-art transfer performance on a variety of downstream
tasks, such as image classification, semi-supervised learning, object detection and segmentation. Furthermore, our
method can also exploit single-centric-object dataset such
as ImageNet and outperforms BYOL by 2.5% with the same
pre-training epochs in linear probing, and surpass current
self-supervised object detection methods on COCO dataset,
demonstrating its universality and potential.

Indexed ByEI
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/47419
Collection模式识别国家重点实验室_图像与视频分析
Affiliation1.National Laboratory of Pattern Recognition, Institute of Automation, CAS, Beijing, China
2.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
3.SenseTime Research
4.Development Research Institute of Guangzhou Smart City, Guangzhou, China
5.S-Lab, Nanyang Technological University
6.Peng Cheng Laboratory, Shenzhen, China
7.Qing Yuan Research Institute, Shanghai Jiao Tong University, Shanghai, China
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Li, Zhaowen,Zhu, Yousong,Yang, Fan,et al. UniVIP: A Unified Framework for Self-Supervised Visual Pre-training[C],2022.
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