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

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.