Training object detectors from few weakly-lab ele d and many unlab ele d images

PATTERN RECOGNITION

卷:120

Article

Yang, Zhaohui¹  Shi, Miaojing²  Xu, Chao¹  Ferrari, Vittorio³  Avrithis, Yannis⁴ 

[1] Peking Univ, Dept Machine Intelligence, Key Lab Machine Percept, Beijing, Peoples R China

[2] Kings Coll London, Dept Informat, London, England

[3] Google Res, Zurich, Switzerland

[4] Univ Rennes, CNRS, IRISA, INRIA, Rennes, France

关键词: Object detection;    Weakly-supervised learning;    Semi-supervised learning;    Unlabelled set;   

DOI  :  10.1016/j.patcog.2021.108164

来源: Elsevier

【 摘 要 】

Weakly-supervised object detection attempts to limit the amount of supervision by dispensing the need for bounding boxes, but still assumes image-level labels on the entire training set. In this work, we study the problem of training an object detector from one or few images with image-level labels and a larger set of completely unlabeled images. This is an extreme case of semi-supervised learning where the labeled data are not enough to bootstrap the learning of a detector. Our solution is to train a weakly supervised student detector model from image-level pseudo-labels generated on the unlabeled set by a teacher classifier model, bootstrapped by region-level similarities to labeled images. Building upon the recent representative weakly-supervised pipeline PCL [1], our method can use more unlabeled images to achieve performance competitive or superior to many recent weakly-supervised detection solutions. Code will be made available at https://github.com/zhaohui-yang/NSOD. (c) 2021 Elsevier Ltd. All rights reserved.

【 授权许可】

Free