The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Video Enhancement with Task-Oriented Flow
Open accessAbstract
Many video enhancement algorithms rely on optical flow to register frames in a video sequence. Precise flow estimation is however intractable; and optical flow itself is often a sub-optimal representation for particular video processing tasks. In this paper, we propose task-oriented flow (TOFlow), a motion representation learned in a self-supervised, task-specific manner. We design a neural network with a trainable motion estimation component and a video processing component, and train them jointly to learn the task-oriented flow. For evaluation, we build Vimeo-90K, a large-scale, high-quality video dataset for low-level video processing. TOFlow outperforms traditional optical flow on standard benchmarks as well as our Vimeo-90K dataset in three video processing tasks: frame interpolation, video denoising/deblocking, and video super-resolution.
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Cites
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 11×
“For example, in temporal frame interpolation, most algorithms first estimate how pixels move between input frames (frame 1 and 3), and then move pixels to the estimated location in the output frame (frame 2) (Baker et al 2011).”
InfluentialA 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
Flow-Guided Feature Aggregation for Video Object Detection
Zhu, X., Wang, Y., Dai, J., Yuan, L., & Wei, Y. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.52 · Cited by 729 · cited 2×
“…(Liu et al 2017), light-field interpolation (Wang et al 2017), novel view synthesis (Zhou et al 2016), eye gaze manipulation (Ganin et al 2016), object detection (Zhu et al 2017), denoising (Wen et al 2017), and super-resolution (Caballero et al 2017; Tao et al 2017; Makansi et al 2017).”
Cited by
Artificial intelligence in the creative industries: a review
Anantrasirichai, N., & Bull, D. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-021-10039-7 · Cited by 793
Low-Light Image and Video Enhancement Using Deep Learning: A Survey
Li, C., Guo, C., Han, L., Jiang, J., Cheng, M.-M., Gu, J., & Loy, C. C. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3126387 · Cited by 657
Basicvsr++: improving video super-resolution with enhanced propagation and alignment
Chan, K. C. K., Zhou, S., Xu, X., & Loy, C. C. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00588 · Cited by 604
Scale-Space Flow for End-to-End Optimized Video Compression
Agustsson, E., Minnen, D., Johnston, N., Ballé, J., Hwang, S. J., & Toderici, G. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00853 · Cited by 317
Seeing Dynamic Scene in the Dark: A High-Quality Video Dataset with Mechatronic Alignment
Wang, R., Xu, X., Fu, C.-W., Lu, J., Yu, B., & Jia, J. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00956 · Cited by 145
Metadata
- Authors
- Xue, T., Chen, B., Wu, J., Wei, D., & Freeman, W. T.
- Venue
- International Journal of Computer Vision
- Published
- 2019-02-12
- Volume
- 127
- Issue
- 8
- Pages
- 1106-1125
- ISSN
- 0920-5691 (ISSN-L)
- 1573-1405
- OpenAlex
- W2769654144
- Cited by
- 1,393
- References
- 3
- FWCI
- 53.74
- Type
- journal article
- Language
- en
- Open access
- green
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