AIM 2020 Challenge on Video Temporal Super-Resolution
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Abstract (arXiv)
Videos in the real-world contain various dynamics and motions that may look unnaturally discontinuous in time when the recordedframe rate is low. This paper reports the second AIM challenge on Video Temporal Super-Resolution (VTSR), a.k.a. frame interpolation, with a focus on the proposed solutions, results, and analysis. From low-frame-rate (15 fps) videos, the challenge participants are required to submit higher-frame-rate (30 and 60 fps) sequences by estimating temporally intermediate frames. To simulate realistic and challenging dynamics in the real-world, we employ the REDS_VTSR dataset derived from diverse videos captured in a hand-held camera for training and evaluation purposes. There have been 68 registered participants in the competition, and 5 teams (one withdrawn) have competed in the final testing phase. The winning team proposes the enhanced quadratic video interpolation method and achieves state-of-the-art on the VTSR task.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Sep 2020.
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Citing papers per year: 2021: 2; 2022: 2; 2023: 1.
5 papers.
- 2021CDFI: Compression-Driven Network Design for Frame Interpolationcited by 55
- 2021NTIRE 2021 Challenge on Video Super-Resolutioncited by 15
- 2022Long-term Video Frame Interpolation via Feature Propagationcited by 8
- 2023Attacking Perceptual Similarity Metricscited by 8
- 2022H-VFI: Hierarchical Frame Interpolation for Videos with Large Motionscited by 4
- 2023Attacking Perceptual Similarity Metrics15 May 2023
- 2022H-VFI: Hierarchical Frame Interpolation for Videos with Large Motions21 Nov 2022
- 2022Long-term Video Frame Interpolation via Feature Propagation29 Mar 2022
- 2021NTIRE 2021 Challenge on Video Super-Resolution30 Apr 2021
- 2021CDFI: Compression-Driven Network Design for Frame Interpolation18 Mar 2021