期刊
JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION
卷 58, 期 -, 页码 532-543出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jvcir.2018.11.020
关键词
Semantic segmentation; Generative adversarial network (GAN); Wasserstein distance; Auxiliary higher-order potential loss
资金
- National Key R&D Program of China [2017YFB1401000]
- National Natural Science Foundation of China [61871378, 61602517]
- Open Project Program of National Laboratory of Pattern Recognition [201800018]
Semantic segmentation plays an important role in a series of high-level computer vision applications. In the state-of-the-art semantic segmentation methods based on fully convolutional neural networks, all label variables are predicted independently from each other, and the restricted field-of-views of the convolutional filters are difficult to capture the long-range information. In this paper, a novel post-processing method based on GAN (Generative Adversarial Network) is explored to reinforce spatial contiguity in the output label maps. With the help of fully connected layers in the discriminator, the GAN can capture the long-range information, and provide an auxiliary higher-order potential loss to the segmentation model, thus the segmentation model has the ability of correcting higher order inconsistencies. Furthermore, the optimization scheme in Wasserstein GAN (WGAN) is adopted to the training process of our model to get better performance and stability. Extensive experiments on public benchmarking database demonstrate the effectiveness of the proposed method. (C) 2018 Elsevier Inc. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据