Binarized Normed Gradients for Objectness Estimation at 300fps
"Explore the Binarized Normed Gradients for Objectness Estimation method, achieving fast and high-quality results in object detection. The approach offers a simple yet effective solution, with impressive speed and performance on challenging benchmark datasets. Discover the innovative approach and its applications in object detection research."
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BING: Binarized Normed Gradients for Objectness Estimation at 300fps 1 Ming-Ming Cheng1 Ziming Zhang2 Wen-Yan Li1 Philip H. S. Torr1 1Torr Vision Group, Oxford University 2Boston University 08:30-10:00, Orals 8A Recognition: Detection, Categorization, and Classification 06/27/2014 BING: Binarized Normed Gradient for Objectness Estimation at 300fps, IEEE CVPR (Oral), 2014, Cheng et. al. 1/7
Motivation: Generic object detection Category specific detectors to evaluate many image windows (Slow). Quickly identifying the object regions before recognize them. 06/27/2014 BING: Binarized Normed Gradient for Objectness Estimation at 300fps, IEEE CVPR (Oral), 2014, Cheng et. al. 2/7
Motivation: What is an object? Category specific detectors to evaluate many image windows (Slow). Quickly identifying the object regions before recognize them. 06/27/2014 BING: Binarized Normed Gradient for Objectness Estimation at 300fps, IEEE CVPR (Oral), 2014, Cheng et. al. 3/7
Motivation: What is an object? An objectness measure A value to reflect how likely an image window covers an object of any category [PAMI 12 Alexe et. al.]. > > Each category specific detectors to evaluate many image windows (Slow). Quickly identifying the object regions before recognize them. 06/27/2014 BING: Binarized Normed Gradient for Objectness Estimation at 300fps, IEEE CVPR (Oral), 2014, Cheng et. al. 4/7
Experimental results Proposal quality on PASCAL VOC 2007 Better detection rate & 1000 times faster 06/27/2014 BING: Binarized Normed Gradient for Objectness Estimation at 300fps, IEEE CVPR (Oral), 2014, Cheng et. al. 5/7
Conclusion and Future Work Conclusions Surprisingly simple, fast, and high quality objectness measure Needs a few atomic operations (i.e. add, bitwise, etc.) per window Test time: 300fps! Training time on the entire VOC07 dataset takes 20 seconds! State of the art results on challenging VOC benchmark 96.2% Detection rate (DR) @ 1K proposals, 99.5% DR @ 5K proposals Generic over classes, training on 6 classes and test on other classes 100+ lines of C++ to implement the algorithm Resources: http://mmcheng.net/bing/ Paper, source code, data, slides, online FAQs, etc. 1000+ source code downloads in 1 week Already got many feedbacks reporting detection speed up free 06/27/2014 BING: Binarized Normed Gradient for Objectness Estimation at 300fps, IEEE CVPR (Oral), 2014, Cheng et. al. 6/7
Thanks for watching Orals 8A, 8:30-10:00, 27th June 06/27/2014 BING: Binarized Normed Gradient for Objectness Estimation at 300fps, IEEE CVPR (Oral), 2014, Cheng et. al. 7/7
