Classification Assisted Segmentation Network For Human Parsing
2019 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP)(2019)
摘要
In human parsing task, it is important to fully exploit global and local structure information and get accurate and coherent results. In this paper, we propose a classification assisted segmentation network, in which a multi-label classification task can obtain the probability of each class in an image that used to learn better weights for parsing task. Our method takes advantages of both the global information from classification and the detail information from segmentation. Experiments demonstrate that our method could efficiently avoid the confusion between similar categories and get more reasonable results. Particularly, it significantly boosts performances of rare categories such as scarf, belt and sunglasses with mean IoU increased by 6.29%.
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关键词
Human parsing, multi-task, deep convolutional nerual network
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