PEPSI : Fast Image Inpainting With Parallel Decoding Network

Min-cheol Sagong, Yong-goo Shin, Seung-wook Kim, Seung Park, Sung-jea Ko; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 11360-11368

Abstract


Recently, a generative adversarial network (GAN)-based method employing the coarse-to-fine network with the contextual attention module (CAM) has shown outstanding results in image inpainting. However, this method requires numerous computational resources due to its two-stage process for feature encoding. To solve this problem, in this paper, we present a novel network structure, called PEPSI: parallel extended-decoder path for semantic inpainting. PEPSI can reduce the number of convolution operations by adopting a structure consisting of a single shared encoding network and a parallel decoding network with coarse and inpainting paths. The coarse path produces a preliminary inpainting result with which the encoding network is trained to predict features for the CAM. At the same time, the inpainting path creates a higher-quality inpainting result using refined features reconstructed by the CAM. PEPSI not only reduces the number of convolution operation almost by half as compared to the conventional coarse-to-fine networks but also exhibits superior performance to other models in terms of testing time and qualitative scores.

Related Material


[pdf]
[bibtex]
@InProceedings{Sagong_2019_CVPR,
author = {Sagong, Min-cheol and Shin, Yong-goo and Kim, Seung-wook and Park, Seung and Ko, Sung-jea},
title = {PEPSI : Fast Image Inpainting With Parallel Decoding Network},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019}
}