Abstract
In Recent years, with the vast development of deep learning techniques, a great deal of effort has been devoted in the computer vision and multimedia community toward the problems of visual object analysis, such as object representation, recognition, detection, identification, etc. Especially at the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) in 2014, the computers have successfully outperformed humans with a lower error rate at image recognition for the first time. However, most of existing algorithms focus more on analyzing objects in a relatively simple and restricted situation, which may perform poorly in natural environments.
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