Automatic learning for object detection

Xiang Zhang, Chao Zhao, Hangzai Luo, Wanqing Zhao, Sheng Zhong, Lei Tang, Jinye Peng, Jianping Fan

Abstract: To alleviate the burden of manual image annotation, we propose an automatic learning method to enable object detection. This method mainly consists of the following three aspects: (1) a novel synthetic data generation strategy, which can automatically generate large-scale synthetic data with bounding-box annotations using only semantic concepts of target categories; (2) self-training paradigm combined with synthetic data generation strategy, which mines more information from the unannotated real data through iterative training to improve the performance of the object detector; (3) a simple and effective pseudo box filtering method, which can purify the quality of pseudo boxes during training. Without using any annotations (i.e., image-level annotations and bounding-box annotations) from the PASCAL VOC dataset, our proposed method can obtain 59.3% and 55.1% mAP on PASCAL VOC 2007 and PASCAL VOC 2012, respectively. We also demonstrate the effectiveness of our method on several datasets, including CUB-200–2011, FGVC Aircraft, Stanford Cars, Bird-Aircraft-Car-Dog, and CBCL StreetScenes.