@inproceedings{36ab4714d9e24d308176a732f52aaebd,
title = "Fast portrait matting using spatial detail-preserving network",
abstract = "Image matting plays an important role in both computer vision and graphics applications. Natural image matting has recently made significant progress with the assistance of powerful Convolutional Neural Networks (CNN). However, it is often time-consuming for pixel-wise label inference. To get higher quality matting in an efficient way, we propose a well-designed SDPNet, which consists of two parallel branches—Semantic Segmentation Branch for half image resolution and Detail-Preserving Branch for full resolution, capturing both the semantic information and image details, respectively. Higher quality alpha matte can be generated while largely reducing the portion of computation. In addition, Spatial Attention Module and Boundary Refinement Module are proposed to extract distinguishable boundary features. Extensive Experiments show that SDPNet provides higher quality results on Portrait Matting benchmark, while obtaining 5x to 20x faster than previous methods.",
keywords = "Deep learning, Detail-preserving, Fast matting, Portrait",
author = "Shaofan Cai and Biao Leng and Guanglu Song and Zheng Ge",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2018.; 25th International Conference on Neural Information Processing, ICONIP 2018 ; Conference date: 13-12-2018 Through 16-12-2018",
year = "2018",
doi = "10.1007/978-3-030-04224-0\_28",
language = "英语",
isbn = "9783030042233",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "329--339",
editor = "Long Cheng and Leung, \{Andrew Chi Sing\} and Seiichi Ozawa",
booktitle = "Neural Information Processing - 25th International Conference, ICONIP 2018, Proceedings",
address = "德国",
}