文件名称:
semantic human matting
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文件大小: 8mb
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上传时间: 2018-11-19
详细说明： 阿里 acm2018论文 Semantic Human Matting Human maing, high quality extraction of humans from natural images, is crucial for a wide variety of applications. Since the maing problem is severely under-constrained, most previous methods require user interactions to take user designated trimaps or scribbles as constraints. is user-in-the-loop nature makes them dicult to be applied to large scale data or time-sensitive scenarios. In this paper, instead of using explicit user input constraints, we employ implicit semantic constraints l earned from data and propose an automatic human maing algorithm Semantic Human Maing (SHM). SHM is the rst algorithm that learns to jointly t both semantic information and high quality details with deep networks. In practice, simultaneously learning both coarse semantics and ne details is challenging. We propose a novel fusion strategy which naturally gives a probabilistic estimation of the alpha mae. We also construct a very large dataset with high quality annotations consisting of 35,513 unique foregrounds to facilitate the learning and evaluation of human maing. Extensive experiments on this dataset and plenty of real images show that SHM achieves comparable results with state-of-the-art interactive matting methods. ...展开详情收缩
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