Joint learning for multi-class object detection

In practice, multiple objects in images are located by consecutively applying one detector for each class and taking the best confident score. In this work, we propose to show the advantage of grouping similar object classes into a hierarchical structure. While this approach has found interest in image classification, it is not analyzed for the object detection task. Each node in the hierarchy represents one decision line. All the decision lines are learned jointly using a novel problem formulation. Based on experiments using PASCAL VOC 2007 dataset, we show that our approach improves detection performance compared to a baseline approach.