The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
DenseNet for dense flow
Open accessAbstract
Classical approaches for estimating optical flow have achieved rapid progress in the last decade. However, most of them are too slow to be applied in real-time video analysis. Due to the great success of deep learning, recent work has focused on using CNNs to solve such dense prediction problems. In this paper, we investigate a new deep architecture, Densely Connected Convolutional Networks (DenseNet), to learn optical flow. This specific architecture is ideal for the problem at hand as it provides shortcut connections throughout the network, which leads to implicit deep supervision. We extend current DenseNet to a fully convolutional network to learn motion estimation in an unsupervised manner. Evaluation results on three standard benchmarks demonstrate that DenseNet is a better fit than other widely adopted CNN architectures for optical flow estimation.
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Densely Connected Convolutional Networks
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566
Flownet: 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
Unsupervised Monocular Depth Estimation with Left-Right Consistency
Godard, C., Aodha, O. M., & Brostow, G. J. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.699 · Cited by 3,270
A Naturalistic Open Source Movie for Optical Flow Evaluation
Butler, D. J., Wulff, J., Stanley, G. B., & Black, M. J. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33783-3_44 · Cited by 1,842
Unsupervised Monocular Depth Estimation with Left-Right Consistency
Godard, C., Mac Aodha, O., & Brostow, G. J. (2016) · arXiv · doi:10.48550/arxiv.1609.03677 · Cited by 28
Cited by
Improved YOLO-V3 with DenseNet for Multi-Scale Remote Sensing Target Detection
Xu, D., & Wu, Y. (2020) · Sensors · doi:10.3390/s20154276 · Cited by 141
Optical Flow Estimation with Deep Learning, a Survey on Recent Advances
Savian, S., Elahi, M., & Tillo, T. (2020) · Unsupervised and Semi-Supervised Learning · doi:10.1007/978-3-030-32583-1_12 · Cited by 29
Papa, L., Alati, E., Russo, P., & Amerini, I. (2022) · IEEE Access · doi:10.1109/access.2022.3170425 · Cited by 17
A Novel Cognitively Inspired Deep Learning Approach to Detect Drivable Areas for Self-driving Cars
Jiang, F., Wang, Z., & Yue, G. (2024) · Cognitive Computation · doi:10.1007/s12559-023-10215-7 · Cited by 9
Metadata
- Authors
- Zhu, Y., & Newsam, S.
- Venue
- 2017 IEEE International Conference on Image Processing (ICIP)
- Publisher
- IEEE
- Published
- 2017-09
- Pages
- 790-794
- ISSN
- 1522-4880 (ISSN-L)
- OpenAlex
- W2736806122
- Cited by
- 314
- References
- 6
- FWCI
- 3.80
- Type
- conference paper
- Language
- en
- Open access
- green
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