The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Patch to the Future: Unsupervised Visual Prediction
Abstract
In this paper we present a conceptually simple but surprisingly powerful method for visual prediction which combines the effectiveness of mid-level visual elements with temporal modeling. Our framework can be learned in a completely unsupervised manner from a large collection of videos. However, more importantly, because our approach models the prediction framework on these mid-level elements, we can not only predict the possible motion in the scene but also predict visual appearances - how are appearances going to change with time. This yields a visual "hallucination" of probable events on top of the scene. We show that our method is able to accurately predict and visualize simple future events, we also show that our approach is comparable to supervised methods for event prediction.
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Citations
Cites
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584
SURF: Speeded Up Robust Features
Bay, H., Tuytelaars, T., & Van Gool, L. (2006) · Lecture Notes in Computer Science · doi:10.1007/11744023_32 · Cited by 14,955
Multi-cue onboard pedestrian detection
Wojek, C., Walk, S., & Schiele, B. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206638 · Cited by 350
Cited by
Unsupervised Learning for Physical Interaction through Video Prediction
Finn, C., Goodfellow, I., & Levine, S. (2016) · arXiv · doi:10.48550/arxiv.1605.07157 · Cited by 759
Generating Videos with Scene Dynamics
Vondrick, C., Pirsiavash, H., & Torralba, A. (2016) · Maryland Shared Open Access Repository · doi:10.13016/m26gih-tnyz · Cited by 441
StyleGAN-V: A Continuous Video Generator with the Price, Image Quality and Perks of StyleGAN2
Skorokhodov, I., Tulyakov, S., & Elhoseiny, M. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00361 · Cited by 199
Crowd Density Forecasting by Modeling Patch-Based Dynamics
Minoura, H., Yonetani, R., Nishimura, M., & Ushiku, Y. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2020.3043169 · Cited by 16
Metadata
- Authors
- Walker, J., Gupta, A., & Hebert, M.
- Venue
- 2014 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2014-06
- Pages
- 3302-3309
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2056120433
- Cited by
- 179
- References
- 4
- FWCI
- 15.86
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Walker, J., Gupta, A., & Hebert, M. (2014). Patch to the Future: Unsupervised Visual Prediction. 2014 IEEE Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.2014.416
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