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Transfer Learning by Ranking for Weakly Supervised Object Annotation

Transfer Learning by Ranking for Weakly Supervised Object Annotation

This video was recorded at British Machine Vision Conference (BMVC), Surrey 2012. Most existing approaches to training object detectors rely on fully supervised learning, which requires the tedious manual annotation of object location in a training set. Recently there has been an increasing interest in developing weakly supervised approach to detector training where the object location is not manually annotated but automatically determined based on binary (weak) labels indicating if a training image contains the object. This is a challenging problem because each image can contain many candidate object locations which partially overlaps the object of interest. Existing approaches focus on how to best utilise the binary labels for object location annotation. In this paper we propose to solve... Show More

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