# Weakly- and Semi-Supervised Object Detection with Expectation-Maximization Algorithm Source: [Weakly- and Semi-Supervised Object Detection with Expectation-Maximization Algorithm]() Collection index: [llms.txt]() Data updated: 2025-10-28T13:56:33.225Z Authors: Ziang Yan, Jian Liang, Weishen Pan, Jin Li, Changshui Zhang Published: 2017-02-28T11:03:39.000Z ## Abstract Object detection when provided image-level labels instead of instance-level labels (i.e., bounding boxes) during training is an important problem in computer vision, since large scale image datasets with instance-level labels are extremely costly to obtain. In this paper, we address this challenging problem by developing an Expectation-Maximization (EM) based object detection method using deep convolutional neural networks (CNNs). Our method is applicable to both the weakly-supervised and semi-supervised settings. Extensive experiments on PASCAL VOC 2007 benchmark show that (1) in the weakly supervised setting, our method provides significant detection performance improvement over current state-of-the-art methods, (2) having access to a small number of strongly (instance-level) annotated images, our method can almost match the performace of the fully supervised Fast RCNN. We share our source code at https://github.com/ZiangYan/EM-WSD. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Object Localization on PASCAL VOC 2007 (trainval)](): Object Localization; PASCAL VOC 2007 (trainval); CorLoc: 65. - [Object Detection on PASCAL VOC 2007 (test)](): Object Detection; PASCAL VOC 2007 (test); mAP: 46.1. ## Original sources and implementation [Original paper]() [PDF]()