NTIRE 2026视频显著性预测挑战赛：方法与成果

NTIRE 2026 Challenge on Video Saliency Prediction: Methods and Results

April 16, 2026

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

cs.AI

摘要

本文对NTIRE 2026视频显著性预测挑战赛进行了全面综述。该竞赛要求参赛者为指定视频序列开发自动显著性图谱预测方法。为此专门构建了包含2000个开放许可多样化视频的全新数据集，通过众包鼠标追踪技术采集了5000余名评估者的注视点及对应显著性图谱数据。竞赛采用公认质量指标对800段测试视频子集进行评估，共吸引20余支团队提交方案，其中7支团队通过最终阶段的代码审核。本次挑战赛所用全部数据已公开：https://github.com/msu-video-group/NTIRE26_Saliency_Prediction。

English

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.