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NTIRE 2026 Challenge on Video Saliency Prediction: Methods and Results

Fuente: arXiv

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Détails bibliographiques
Auteurs principaux:	Moskalenko, Andrey, Bryncev, Alexey, Kosmynin, Ivan, Shilovskaya, Kira, Erofeev, Mikhail, Vatolin, Dmitry, Timofte, Radu, Wang, Kun, Hu, Yupeng, Li, Zhiran, Liu, Hao, Xiang, Qianlong, Nie, Liqiang, Chaldaiopoulos, Konstantinos, Efthymiou, Niki, Zlatintsi, Athanasia, Filntisis, Panagiotis, Pastra, Katerina, Maragos, Petros, Yang, Li, Zhan, Gen, Liao, Yiting, Zhang, Yabin, Liu, Yuxin, Wu, Xu, Zheng, Yunheng, Li, Linze, He, Kun, Wu, Cong, Zhu, Xuefeng, Xu, Tianyang, Wu, Xiaojun, Zhao, Wenzhuo, Fu, Keren, Li, Gongyang, Shi, Shixiang, Chen, Jianlin, Ling, Haibin, Jiang, Yaoxin, Xu, Guoyi, Liu, Jiajia, Shi, Yaokun, Tu, Jiachen
Format:	Preprint
Publié:	2026
Sujets:	Computer Vision and Pattern Recognition Human-Computer Interaction Multimedia I.4.6; I.2.10
Accès en ligne:	Acceder al recurso
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_version_	1866918450575704064
author	Moskalenko, Andrey Bryncev, Alexey Kosmynin, Ivan Shilovskaya, Kira Erofeev, Mikhail Vatolin, Dmitry Timofte, Radu Wang, Kun Hu, Yupeng Li, Zhiran Liu, Hao Xiang, Qianlong Nie, Liqiang Chaldaiopoulos, Konstantinos Efthymiou, Niki Zlatintsi, Athanasia Filntisis, Panagiotis Pastra, Katerina Maragos, Petros Yang, Li Zhan, Gen Liao, Yiting Zhang, Yabin Liu, Yuxin Wu, Xu Zheng, Yunheng Li, Linze He, Kun Wu, Cong Zhu, Xuefeng Xu, Tianyang Wu, Xiaojun Zhao, Wenzhuo Fu, Keren Li, Gongyang Shi, Shixiang Chen, Jianlin Ling, Haibin Jiang, Yaoxin Xu, Guoyi Liu, Jiajia Shi, Yaokun Tu, Jiachen
author_facet	Moskalenko, Andrey Bryncev, Alexey Kosmynin, Ivan Shilovskaya, Kira Erofeev, Mikhail Vatolin, Dmitry Timofte, Radu Wang, Kun Hu, Yupeng Li, Zhiran Liu, Hao Xiang, Qianlong Nie, Liqiang Chaldaiopoulos, Konstantinos Efthymiou, Niki Zlatintsi, Athanasia Filntisis, Panagiotis Pastra, Katerina Maragos, Petros Yang, Li Zhan, Gen Liao, Yiting Zhang, Yabin Liu, Yuxin Wu, Xu Zheng, Yunheng Li, Linze He, Kun Wu, Cong Zhu, Xuefeng Xu, Tianyang Wu, Xiaojun Zhao, Wenzhuo Fu, Keren Li, Gongyang Shi, Shixiang Chen, Jianlin Ling, Haibin Jiang, Yaoxin Xu, Guoyi Liu, Jiajia Shi, Yaokun Tu, Jiachen
contents	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.
format	Preprint
id	arxiv_https___arxiv_org_abs_2604_14816
institution	arXiv
publishDate	2026
record_format	arxiv
spellingShingle	NTIRE 2026 Challenge on Video Saliency Prediction: Methods and Results Moskalenko, Andrey Bryncev, Alexey Kosmynin, Ivan Shilovskaya, Kira Erofeev, Mikhail Vatolin, Dmitry Timofte, Radu Wang, Kun Hu, Yupeng Li, Zhiran Liu, Hao Xiang, Qianlong Nie, Liqiang Chaldaiopoulos, Konstantinos Efthymiou, Niki Zlatintsi, Athanasia Filntisis, Panagiotis Pastra, Katerina Maragos, Petros Yang, Li Zhan, Gen Liao, Yiting Zhang, Yabin Liu, Yuxin Wu, Xu Zheng, Yunheng Li, Linze He, Kun Wu, Cong Zhu, Xuefeng Xu, Tianyang Wu, Xiaojun Zhao, Wenzhuo Fu, Keren Li, Gongyang Shi, Shixiang Chen, Jianlin Ling, Haibin Jiang, Yaoxin Xu, Guoyi Liu, Jiajia Shi, Yaokun Tu, Jiachen Computer Vision and Pattern Recognition Human-Computer Interaction Multimedia I.4.6; I.2.10 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.
title	NTIRE 2026 Challenge on Video Saliency Prediction: Methods and Results
topic	Computer Vision and Pattern Recognition Human-Computer Interaction Multimedia I.4.6; I.2.10
url	https://arxiv.org/abs/2604.14816