W2F: A Weakly-Supervised to Fully-Supervised Framework for Object Detection
KAUST DepartmentVisual Computing Center (VCC)
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Electrical Engineering Program
Permanent link to this recordhttp://hdl.handle.net/10754/653001
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AbstractWeakly-supervised object detection has attracted much attention lately, since it does not require bounding box annotations for training. Although significant progress has also been made, there is still a large gap in performance between weakly-supervised and fully-supervised object detection. Recently, some works use pseudo ground-truths which are generated by a weakly-supervised detector to train a supervised detector. Such approaches incline to find the most representative parts of objects, and only seek one ground-truth box per class even though many same-class instances exist. To overcome these issues, we propose a weakly-supervised to fully-supervised framework, where a weakly-supervised detector is implemented using multiple instance learning. Then, we propose a pseudo ground-truth excavation (PGE) algorithm to find the pseudo ground-truth of each instance in the image. Moreover, the pseudo ground-truth adaptation (PGA) algorithm is designed to further refine the pseudo ground-truths from PGE. Finally, we use these pseudo ground-truths to train a fully-supervised detector. Extensive experiments on the challenging PASCAL VOC 2007 and 2012 benchmarks strongly demonstrate the effectiveness of our framework. We obtain 52.4% and 47.8% mAP on VOC2007 and VOC2012 respectively, a significant improvement over previous state-of-the-art methods.
CitationZhang Y, Bai Y, Ding M, Li Y, Ghanem B (2018) W2F: A Weakly-Supervised to Fully-Supervised Framework for Object Detection. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Available: http://dx.doi.org/10.1109/CVPR.2018.00103.
SponsorsThis work was supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research and by Natural Science Foundation of China, Grant No. 61603372.
Conference/Event name31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018