Discovering visual knowledge from weakly labeled data are crucial to scale up computer vision recognition system, since it is expensive to obtain fully labeled data for a large number of concept categories while the weakly labeled data could be collected from the Internet cheaply and massively.
In this paper we proposes a scalable approach to discover visual concepts from weakly labeled image collections, with thousands of visual concept detectors learned. Then we show that the learned detectors could be applied to recognize concepts at image-level and to detect concepts at image region-level accurately.
Under domain-selected supervision, we further evaluate the learned concepts for scene recognition on SUN database and for object detection on Pascal VOC 2007. It shows promising performance compared to the fully supervised and weakly supervised methods.