NTIRE 2026 Challenge on Video Saliency Prediction: Methods and Results
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Abstract (arXiv)
This paper presents an overview of the NTIRE 2026 Challenge on Video Saliency Prediction. The goal of the challenge participants was to develop automatic saliency map prediction methods for the provided video sequences. The novel dataset of 2,000 diverse videos with an open license was prepared for this challenge. The fixations and corresponding saliency maps were collected using crowdsourced mouse tracking and contain viewing data from over 5,000 assessors. Evaluation was performed on a subset of 800 test videos using generally accepted quality metrics. The challenge attracted over 20 teams making submissions, and 7 teams passed the final phase with code review. All data used in this challenge is made publicly available - https://github.com/msu-video-group/NTIRE26_Saliency_Prediction.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 54 of its 107 references that are in the map. First posted on arXiv 16 Apr 2026.
Builds on
54 of 107 references.
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50 most cited of 54 All 54 (JSON)
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Citing papers per year: 2026: 10.
10 papers.
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- 2026Pixel-Space Diffusion Transformers20 Jul 2026
- 2026The Third Challenge on Image Denoising at NTIRE 2026: Methods and Results14 Jun 2026
- 2026The First Controllable Bokeh Rendering Challenge at NTIRE 20266 May 2026
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- 2026Robust Deepfake Detection, NTIRE 2026 Challenge: Report27 Apr 2026
- 2026The First Challenge on Remote Sensing Infrared Image Super-Resolution at NTIRE 2026: Benchmark Results and Method Overview23 Apr 2026
- 2026Low Light Image Enhancement Challenge at NTIRE 202619 Apr 2026
- 2026The First Challenge on Mobile Real-World Image Super-Resolution at NTIRE 2026: Benchmark Results and Method Overview19 Apr 2026
- 2026NTIRE 2026 Rip Current Detection and Segmentation (RipDetSeg) Challenge Report18 Apr 2026