NTIRE 2026 Challenge on Video Saliency Prediction: Methods and Results
This challenge provides a new benchmark and dataset for the video saliency prediction community, but is an incremental contribution as it follows the format of previous NTIRE challenges.
The NTIRE 2026 Challenge on Video Saliency Prediction introduced a new dataset of 2,000 videos with crowdsourced saliency maps from over 5,000 assessors, and evaluated 7 finalist teams on 800 test videos using standard metrics.
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.