The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Dense Optical Flow Prediction from a Static Image
Abstract
Given a scene, what is going to move, and in what direction will it move? Such a question could be considered a non-semantic form of action prediction. In this work, we present a convolutional neural network (CNN) based approach for motion prediction. Given a static image, this CNN predicts the future motion of each and every pixel in the image in terms of optical flow. Our CNN model leverages the data in tens of thousands of realistic videos to train our model. Our method relies on absolutely no human labeling and is able to predict motion based on the context of the scene. Because our CNN model makes no assumptions about the underlying scene, it can predict future optical flow on a diverse set of scenarios. We outperform all previous approaches by large margins.
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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
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Large-Scale Video Classification with Convolutional Neural Networks
Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., & Fei-Fei, L. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.223 · Cited by 6,354
Hmdb: a large video database for human motion recognition
Kuehne, H., Jhuang, H., Garrote, E., Poggio, T., & Serre, T. (2011) · 2011 International Conference on Computer Vision · doi:10.1109/iccv.2011.6126543 · Cited by 3,965
Action Recognition with Improved Trajectories
Wang, H., & Schmid, C. (2013) · 2013 IEEE International Conference on Computer Vision · doi:10.1109/iccv.2013.441 · Cited by 3,407
A Database and Evaluation Methodology for Optical Flow
Baker, S., Scharstein, D., Lewis, J. P., Roth, S., Black, M. J., & Szeliski, R. (2011) · International Journal of Computer Vision · doi:10.1007/s11263-010-0390-2 · Cited by 2,205 · cited 1×
“1 ar X iv :1 50 5.”
Unsupervised Learning of Video Representations using LSTMs
Srivastava, N., Mansimov, E., & Salakhutdinov, R. (2015) · arXiv · doi:10.48550/arxiv.1502.04681 · Cited by 1,861
PANDA: Pose Aligned Networks for Deep Attribute Modeling
Zhang, N., Paluri, M., Ranzato, M., Darrell, T., & Bourdev, L. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.212 · Cited by 495
Cited by
ReconNet: Non-Iterative Reconstruction of Images from Compressively Sensed Measurements
Kulkarni, K., Lohit, S., Turaga, P., Kerviche, R., & Ashok, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.55 · Cited by 768
Self-supervised learning for medical image analysis using image context restoration
Chen, L., Bentley, P., Mori, K., Misawa, K., Fujiwara, M., & Rueckert, D. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.101539 · Cited by 575
Look-Ahead Before You Leap: End-to-End Active Recognition by Forecasting the Effect of Motion
Jayaraman, D., & Grauman, K. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46454-1_30 · Cited by 75
“Predicting related features There is recent interest in “visual prediction” problems in various contexts [41,33,25,19,46,20,23,42], often using convolutional neural networks (CNNs).”
BackgroundMemFlow: Optical Flow Estimation and Prediction with Memory
Dong, Q., & Fu, Y. (2024) · arXiv · doi:10.48550/arxiv.2404.04808 · Cited by 49
Metadata
- Authors
- Walker, J., Gupta, A., & Hebert, M.
- Venue
- 2015 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2015-12
- Pages
- 2443-2451
- OpenAlex
- W1930563420
- Cited by
- 163
- References
- 8
- FWCI
- 7.07
- Type
- conference paper
- Language
- en
- Open access
- closed
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