Training Object Detectors from Few Weakly-Labeled and Many Unlabeled Images

Zhaohui Yang; Miaojing Shi; Chao Xu; Vittorio Ferrari; Yannis Avrithis

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arXiv · 1912.00384

Training Object Detectors from Few Weakly-Labeled and Many Unlabeled Images

Zhaohui Yang·Miaojing Shi·Chao Xu·Vittorio Ferrari·Yannis Avrithis

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Abstract

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, our method can use more unlabeled images to achieve performance competitive or superior to many recent weakly-supervised detection solutions.

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Zhaohui Yang, Miaojing Shi, Chao Xu, Vittorio Ferrari, Yannis Avrithis. 2019-12-01. Training Object Detectors from Few Weakly-Labeled and Many Unlabeled Images. https://arxiv.org/abs/1912.00384

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