Name
Title | ||
|---|---|---|
Ad-Net: Attention Guided Network For Optical Flow Estimation Using Dilated Convolution |
Abstract | ||
|---|---|---|
Variational models for optical flow estimation usually define an energy function that contains prior assumptions to explore rudimentary statistics of images However, such methods cannot learn motion knowledge from the pre-prepared data and have many parameters that need to be set manually. Nowadays, convolutional neural networks (CNNs) have been used in optical flow estimation successfully, which can learn weights from the training dataset and can predict optical flow end-to-end. In this paper, we propose an attention guided network for learning optical flow, named AD-Net, which contains several attention units for modelling the relativities between the channels. Further, we introduce dilated convolution into supervised network for reducing the loss of motion details. In addition, some prior auxiliary constraints are embedded in the supervised network as auxiliary loss terms. Our proposed approach is tested on MPI-Sintel and KITTI2012 datasets and can preserve motion edges and details effectively. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/icassp.2019.8682229 | 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) |
Keywords | Field | DocType |
Optical flow estimation, deep learning, attention mechanism, dilated convolution | Pattern recognition,Convolutional neural network,Computer science,Convolution,Communication channel,Optical flow estimation,Artificial intelligence,Optical imaging,Optical flow | Conference |
ISSN | Citations | PageRank |
1520-6149 | 1 | 0.35 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mingliang Zhai | 1 | 12 | 4.31 |
| Xue-Zhi Xiang | 2 | 12 | 7.35 |
| Rongfang Zhang | 3 | 11 | 3.22 |
| Ning Lv | 4 | 31 | 11.32 |
| Abdulmotaleb El-Saddik | 5 | 2416 | 248.48 |