Visual surveillance aims to stably detect a foreground object using continuous image acquired from fixed camera. Recent deep learning methods based on supervised show superior performance compared classical background subtraction algorithms. However, there is still room for improvement in static foreground, dynamic background, hard shadow, illumination changes, camouflage, etc. In addition, mos...

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The scarcity of balanced and annotated datasets has been a recurring problem in medical image analysis. Several researchers have tried to fill this gap employing dataset synthesis with adversarial networks (GANs). Breast magnetic resonance imaging (MRI) provides complex, texture-rich images, the same annotation shortage issues, for which, best our knowledge, no previous work synthesizing data. ...

In the Beijing plain, the long-term groundwater overexploitation, exploitation, and the utilization of superficial urban space have led to land subsidence. In this study, the spatial–temporal analysis of land subsidence in Beijing was assessed by using the small baseline subset (SBAS) interferometric synthetic aperture radar (InSAR) technique based on 47 TerraSAR-X SAR images from 2010 to 2015....

Automated data augmentation has shown superior performance in image recognition. Existing works search for dataset-level policies without considering individual sample variations, which are likely to be sub-optimal. On the other hand, learning different samples naively could greatly increase computing cost. In this paper, we learn a sample-aware policy efficiently by formulating it as reweighti...