@article{Wu2023, 
author = {Jun-xian Wu and Yuan-lie He},
title = {Channel Attentive Self-supervised Network for Monocular Depth Estimation},
year = {2023},
journal = {Journal of Guangdong University of Technology},
volume = {40},
number = {2},
pages = {22-29},
keywords = {monocular depth estimation, attention mechanism, self-supervised deep-learning},
url = {https://www.sciopen.com/article/10.12052/gdutxb.210139},
doi = {10.12052/gdutxb.210139},
abstract = {A new method is proposed for self-supervised monocular depth estimation. Although recent methods have been able to produce high-precision depth maps, previous work has ignored channel-wise information in the image. To solve this problem, Channel Attention is introduced and  improvements made in two aspects of the network structure: (a) a Squeeze-and-Excitation (SE) block is injected into the corresponding networks to capture the channel relation in feature map; (b) a Channel Attention Dense Connection (CADC) block is applied to combine multi-scale features and recalibrate channel-wise feature. Experiments on the KITTI dataset show the effectiveness of the proposed approach, which outperforms quantitatively and qualitatively the state-of-the-art Self-Supervised Depth Estimation methods.}
}