The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
Abstract
The FlowNet demonstrated that optical flow estimation can be cast as a learning problem. However, the state of the art with regard to the quality of the flow has still been defined by traditional methods. Particularly on small displacements and real-world data, FlowNet cannot compete with variational methods. In this paper, we advance the concept of end-to-end learning of optical flow and make it work really well. The large improvements in quality and speed are caused by three major contributions: first, we focus on the training data and show that the schedule of presenting data during training is very important. Second, we develop a stacked architecture that includes warping of the second image with intermediate optical flow. Third, we elaborate on small displacements by introducing a subnetwork specializing on small motions. FlowNet 2.0 is only marginally slower than the original FlowNet but decreases the estimation error by more than 50%. It performs on par with state-of-the-art methods, while running at interactive frame rates. Moreover, we present faster variants that allow optical flow computation at up to 140fps with accuracy matching the original FlowNet.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Stacked Hourglass Networks for Human Pose Estimation
Newell, A., Yang, K., & Deng, J. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46484-8_29 · Cited by 5,634
Bengio, Y., Louradour, J., Collobert, R., & Weston, J. (2009) · Proceedings of the 26th Annual International Conference on Machine Learning · doi:10.1145/1553374.1553380 · Cited by 5,366 · cited 1×
“The strategy of training machine learning models on a series of gradually increasing tasks is known as curriculum learning [5].”
BackgroundFlownet: Learning optical flow with convolutional networks
Dosovitskiy, A., Fischer, P., Ilg, E., Hausser, P., Hazirbas, C., Golkov, V., Smagt, P. V. D., Cremers, D., & Brox, T. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.316 · Cited by 4,313
Self-citationLearning and development in neural networks: the importance of starting small
Elman, J. L. (1993) · Cognition · doi:10.1016/0010-0277(93)90058-4 · Cited by 1,988 · cited 1×
“The idea dates back at least to Elman [11], who showed that both the evolution of tasks and the network architectures can be beneficial in the language processing scenario.”
BackgroundHuman Pose Estimation with Iterative Error Feedback
Carreira, J., Agrawal, P., Fragkiadaki, K., & Malik, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.512 · Cited by 805
Research works printed but not linked: 28
- 10.1007/978-3-319-49409-8_1 · DOI · Paper page
- 10.1109/CVPR.2015.7298607 · DOI · Paper page
- 10.1109/ICCV.2013.175 · DOI · Paper page
- tran, 2016, Deep end2end voxe12voxel prediction (the 3rd workshop on deep learning in computer vision), IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- 10.1109/CVPR.2016.459 · DOI
- güney, 2016, Deep discrete flow, Asian Conference on Computer Vision (ACCV)
- 10.1109/ICCV.2015.374 · DOI · Paper page
- 10.1007/978-3-319-46493-0_29 · DOI · Paper page
- lucas, 1981, An iterative image registration technique with an application to stereo vision, Proceedings of the 7th Int Joint Conference on Artificial Intelligence (IJCAI)
- 10.1109/CVPR.2016.438 · DOI · Paper page
- 10.1109/TPAMI.2013.242 · DOI · Paper page
- teney, 2016, Learning to extract motion from videos in convolutional neural networks
- bao, 2014, Fast edge-preserving patch-match for large displacement optical flow, IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- 10.1007/978-3-642-15555-0_21 · DOI · Paper page
- bailer, 2016, CNN based patch matching for optical flow with thresholded hinge loss
- brox, 2004, High accuracy optical flow estimation based on a theory for warping, European Conference on Computer Vision (ECCV)
- thewlis, 2016, Fully-trainable deep matching, British Machine Vision Conference (BMVC)
- 10.1109/TPAMI.2010.143 · DOI · Paper page
- 10.1109/ICCV.2015.457 · DOI · Paper page
- chen, 2016, Trainable nonlinear reaction diffusion: A flexible framework for fast and effective image restoration, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 1
- 10.1109/ICIP.2016.7532634 · DOI
- ranjan, 2016, Optical flow estimation using a spatial pyramid network
- 10.1007/978-3-319-46466-4_19 · DOI · Paper page
- 10.1109/CVPR.2015.7298720 · DOI · Paper page
- simonyan, 2014, Two-stream convolutional networks for action recognition in videos, Conf on Neural Information Process ins Systems (NIPS)
- savva, 2015, Semantically-Enriched 3D Models for Common-sense Knowledge (Workshop on Functionality, Physics, Intentionality and Causality), IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- sundaram, 2010, Dense point trajectories by gpu-accelerated large displacement optical flow, European Conference on Computer Vision (ECCV)
- soomro, 2013, Ucf101 A Dataset of 101 Human Actions Classes from Videos in the Wild
Cited by
Deep High-Resolution Representation Learning for Human Pose Estimation
Sun, K., Xiao, B., Liu, D., & Wang, J. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00584 · Cited by 5,984
(2020) · doi:10.1007/978-3-030-58536-5_24 · Cited by 2,824
Digging Into Self-Supervised Monocular Depth Estimation
Godard, C., Aodha, O. M., Firman, M., & Brostow, G. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00393 · Cited by 2,561
Self-Supervised Visual Feature Learning With Deep Neural Networks: A Survey
Jing, L., & Tian, Y. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.2992393 · Cited by 2,036
Zhou, X., Koltun, V., & Krähenbühl, P. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58548-8_28 · Cited by 1,428
Metadata
- Authors
- Ilg, E., Mayer, N., Saikia, T., Keuper, M., Dosovitskiy, A., & Brox, T.
- Venue
- 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 1647-1655
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2560474170
- Cited by
- 3,152
- References
- 5
- FWCI
- 94.24
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks. https://app.cochat.ai/papers/10.1109/cvpr.2017.179
- The paper
- Ilg, E., Mayer, N., Saikia, T., Keuper, M., Dosovitskiy, A., & Brox, T. (2017). FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2017.179
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
