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.
@article{arxiv.2604.14816,
title = {NTIRE 2026 Challenge on Video Saliency Prediction: Methods and Results},
author = {Andrey Moskalenko and Alexey Bryncev and Ivan Kosmynin and Kira Shilovskaya and Mikhail Erofeev and Dmitry Vatolin and Radu Timofte and Kun Wang and Yupeng Hu and Zhiran Li and Hao Liu and Qianlong Xiang and Liqiang Nie and Konstantinos Chaldaiopoulos and Niki Efthymiou and Athanasia Zlatintsi and Panagiotis Filntisis and Katerina Pastra and Petros Maragos and Li Yang and Gen Zhan and Yiting Liao and Yabin Zhang and Yuxin Liu and Xu Wu and Yunheng Zheng and Linze Li and Kun He and Cong Wu and Xuefeng Zhu and Tianyang Xu and Xiaojun Wu and Wenzhuo Zhao and Keren Fu and Gongyang Li and Shixiang Shi and Jianlin Chen and Haibin Ling and Yaoxin Jiang and Guoyi Xu and Jiajia Liu and Yaokun Shi and Jiachen Tu},
journal= {arXiv preprint arXiv:2604.14816},
year = {2026}
}