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.

翻译：本文概述了NTIRE 2026视频显著性预测挑战赛。参赛者的目标是针对所提供的视频序列开发自动显著性图预测方法。为此挑战赛准备了一个包含2,000个多样化视频的新数据集，该数据集采用开放许可。注视点及相应的显著性图通过众包鼠标追踪收集，包含来自超过5,000名评估者的观看数据。评估在800个测试视频的子集上进行，采用普遍接受的质量指标。此次挑战吸引了20多支团队提交作品，其中7支团队通过了包含代码审查的最后阶段。挑战中使用的所有数据均已公开，访问地址为：https://github.com/msu-video-group/NTIRE26_Saliency_Prediction。