Semi-Supervised Self-Training of Object Detection Models

Citations Over TimeTop 1% of 2005 papers

Abstract

The construction of appearance-based object detection systems is time-consuming and difficult because a large number of training examples must be collected and manually labeled in order to capture variations in object appearance. Semi-supervised training is a means for reducing the effort needed to prepare the training set by training the model with a small number of fully labeled examples and an additional set of unlabeled or weakly labeled examples. In this work we present a semi-supervised approach to training object detection systems based on self-training. We implement our approach as a wrapper around the training process of an existing object detector and present empirical results. The key contributions of this empirical study is to demonstrate that a model trained in this manner can achieve results comparable to a model trained in the traditional manner using a much larger set of fully labeled data, and that a training data selection metric that is defined independently of the detector greatly outperforms a selection metric based on the detection confidence generated by the detector.

Related Papers

• → Training Systems Concept for the Armored Family of Vehicles with Consideration of the Roles of Embedded Training and Stand-Alone Training Devices(1988)1 cited
• Hydroelectric Construction Jobs and related Training | Hydro Northern Training Initiative | Manitoba Competitiveness, Training and Trade(2004)
• 介護実習IIにおける実習計画表の活用の検討 : 個別援助技術実習と介護総合実習との比較 ; Consideration of utilization of a training agenda in care practical training II － The comparative of individual assistance technical training and total practice training －(2016)
• Effects of Training Input on Training Performance of Airline Service Training - Focused on Mediating Role of Training Process -(2012)