2016 IEEE Winter Conference on Applications of Computer Vision (WACV) (2016)
Lake Placid, NY, USA
March 7, 2016 to March 10, 2016
Xi Stephen Chen , Department of Computer Science, University of Maryland, College Park, MD 20742
He He , Department of Computer Science, University of Maryland, College Park, MD 20742
Larry S. Davis , Department of Computer Science, University of Maryland, College Park, MD 20742
We propose a novel general strategy for object detection. Instead of passively evaluating all object detectors at all possible locations in an image, we develop a divide-and-conquer approach by actively and sequentially evaluating contextual cues related to the query based on the scene and previous evaluations - like playing a "20 Questions" game - to decide where to search for the object. We formulate the problem as a Markov Decision Process and learn a search policy by reinforcement learning. To demonstrate the efficacy of our generic algorithm, we apply the 20 questions approach in the recent framework of simultaneous object detection and segmentation. Experimental results on the Pascal VOC dataset show that our algorithm reduces about 45.3% of the object proposals and 36% of average evaluation time while achieving better average precision compared to exhaustive search.
Context, Search problems, Object detection, Detectors, Context modeling, Roads, Training
X. S. Chen, H. He and L. S. Davis, "Object detection in 20 questions," 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Placid, NY, USA, 2016, pp. 1-9.