The DOI matches its Crossref record; 41 of the 44 references it cites were found in a registry or an index.
FlowNet: Learning Optical Flow with Convolutional Networks
Abstract
Convolutional neural networks (CNNs) have recently been very successful in a variety of computer vision tasks, especially on those linked to recognition. Optical flow estimation has not been among the tasks CNNs succeeded at. In this paper we construct CNNs which are capable of solving the optical flow estimation problem as a supervised learning task. We propose and compare two architectures: a generic architecture and another one including a layer that correlates feature vectors at different image locations. Since existing ground truth data sets are not sufficiently large to train a CNN, we generate a large synthetic Flying Chairs dataset. We show that networks trained on this unrealistic data still generalize very well to existing datasets such as Sintel and KITTI, achieving competitive accuracy at frame rates of 5 to 10 fps.
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“On the other hand, Scene Flow model is trained in highly dynamic synthetic environments, i.e. able to estimate large flow magnitude caused by moving objects.”
From DF-VO: What Should Be Learnt for Visual Odometry? (2021) · cites this paper as Dosovitskiy et al. 2015
In the citing paper
“LiteFlowNet shows good generalization ability. LiteFlowNet trained on a synthetic dataset (Scene Flow(Dosovitskiy et al. 2015)) can generalize well in realworld scenarios, though sometimes artifacts present in some regions.”
From DF-VO: What Should Be Learnt for Visual Odometry? (2021) · cites this paper as Dosovitskiy et al. 2015
In the citing paper
“Uzunova et al. (78) designed a network for the registration between 2D brain MRI and 2D cardiac MRI. Their ground truth is generated utilizing statistical appearance models (SAMs). They adapted FlowNet (79) architecture and obtained outperforming results.”
From A review of deep learning-based deformable medical image registration (2022) · cites this paper as [79]
Citations
Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684
Labaca-Castro, R. (2023) · Springer eBooks · doi:10.1007/978-3-658-40442-0_9 · Cited by 21,119
GAN（Generative Adversarial Nets）
淳司, 柴. (2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 20,507
Visualizing and understanding convolutional networks
Zeiler, M. D., & Fergus, R. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10590-1_53 · Cited by 15,839
Backpropagation Applied to Handwritten Zip Code Recognition
LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989) · Neural Computation · doi:10.1162/neco.1989.1.4.541 · Cited by 11,891
Caffe: Convolutional Architecture for Fast Feature Embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · Proceedings of the 22nd ACM international conference on Multimedia · doi:10.1145/2647868.2654889 · Cited by 11,077
Horn, B. K. P., & Schunck, B. G. (1981) · Artificial Intelligence · doi:10.1016/0004-3702(81)90024-2 · Cited by 10,312
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DF-VO: What Should Be Learnt for Visual Odometry?
Zhan, H., Weerasekera, C. S., Bian, J.-W., Garg, R., & Reid, I. (2021) · arXiv · doi:10.48550/arxiv.2103.00933 · Cited by 31
Joint Deep Learning of Foreground, Background and Shape for Robust Contextual Segmentation
Ravishankar, H., Thiruvenkadam, S., Venkataramani, R., & Vaidya, V. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-59050-9_49 · Cited by 23
Kadi, H. A., & Terzić, K. (2023) · Sensors · doi:10.3390/s23052389 · Cited by 27
“In addition, some of the mentioned BC methods train a goal-conditioned Transporter Network [34,104,234] or flow network [237,268] individually to improve data efficiency, yet with P&P action primitives.”
MethodHaq, E. U., Huarong, X., Xuhui, C., Wanqing, Z., Jianping, F., & Abid, F. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-019-08167-y · Cited by 12
Deep learning for asteroids autonomous terrain relative navigation
Mancini, P., Cannici, M., & Matteucci, M. (2023) · Advances in Space Research · doi:10.1016/j.asr.2022.04.020 · Cited by 10
Video Object Detection Base on RGB and Optical Flow Analysis
Zhang, S., Wang, T., Wang, C., Wang, Y., Shan, G., & Snoussi, H. (2019) · 2019 2nd China Symposium on Cognitive Computing and Hybrid Intelligence (CCHI) · doi:10.1109/cchi.2019.8901921 · Cited by 7
Transformer-Based Model for Monocular Visual Odometry: A Video Understanding Approach
Françani, A. O., & Maximo, M. R. O. A. (2023) · arXiv · doi:10.48550/arxiv.2305.06121 · Cited by 38
“DeepVO [4] is a state-of-the-art end-to-end architecture that extracts features from an image pair (consecutive frames) using a CNN with pretrained FlowNet weights [26], followed by an RNN with LSTM cells to handle the temporal information of the estimated poses.”
Metadata
- Authors
- Dosovitskiy, A., Fischer, P., Ilg, E., Hausser, P., Hazirbas, C., Golkov, V., Smagt, P. V. D., Cremers, D., & Brox, T.
- Affiliations
- University of Freiburg (DE)
- Technical University of Munich (DE)
- Venue
- 2015 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2015-12
- Pages
- 2758-2766
- OpenAlex
- W764651262
- Cited by
- 4,313
- References
- 44
- Citations by year
- 2026: 182 · 2025: 342 · 2024: 462 · 2023: 550 · 2022: 559 · 2021: 647 · 2020: 579 · 2019: 469 · 2018: 278 · 2017: 184 · 2016: 57 · 2015: 3
- FWCI
- 71.36
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- Funders
- Deutsche Telekom Stiftung
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Keywords
- optical flow, convolutional neural networks, supervised learning, synthetic training data, KITTI, video frame rate
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