@inproceedings{5e123582832b42b0a4d4385900c4fe96,
title = "High-Performance Light Field Reconstruction with Channel-wise and SAI-wise Attention",
abstract = "Light field (LF) images provide rich information and are suitable for high-level computer vision applications. To acquire capabilities of modeling the correlated information of LF, most of the previous methods have to stack several convolutional layers to improve the feature representation and result in heavy computation and large model sizes. In this paper, we propose channel-wise and SAI-wise attention modules to enhance the feature representation at a low cost. The channel-wise attention module helps to focus on important channels while the SAI-wise attention module guides the network to pay more attention to informative SAIs. The experimental results demonstrate that the baseline network can achieve better performance with the aid of the attention modules.",
keywords = "Deep learning, Image processing, Light field",
author = "Zexi Hu and Chung, {Yuk Ying} and Zandavi, {Seid Miad} and Wanli Ouyang and Xiangjian He and Yuefang Gao",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2019.; 26th International Conference on Neural Information Processing, ICONIP 2019 ; Conference date: 12-12-2019 Through 15-12-2019",
year = "2019",
doi = "10.1007/978-3-030-36802-9_14",
language = "English",
isbn = "9783030368012",
series = "Communications in Computer and Information Science",
publisher = "Springer",
pages = "118--126",
editor = "Tom Gedeon and Wong, {Kok Wai} and Minho Lee",
booktitle = "Neural Information Processing - 26th International Conference, ICONIP 2019, Proceedings",
}