This paper reviews the Challenge on Video Saliency Prediction at AIM 2024. The goal of the participants was to develop a method for predicting accurate saliency maps for the provided set of video sequences. Saliency maps are widely exploited in various applications, including video compression, quality assessment, visual perception studies, the advertising industry, etc. For this competition, a previously unused large-scale audio-visual mouse saliency (AViMoS) dataset of 1500 videos with more than 70 observers per video was collected using crowdsourced mouse tracking. The dataset collection methodology has been validated using conventional eye-tracking data and has shown high consistency. Over 30 teams registered in the challenge, and there are 7 teams that submitted the results in the final phase. The final phase solutions were tested and ranked by commonly used quality metrics on a private test subset. The results of this evaluation and the descriptions of the solutions are presented in this report. All data, including the private test subset, is made publicly available on the challenge homepage - https://challenges.videoprocessing.ai/challenges/video-saliency-prediction.html.
@article{arxiv.2409.14827,
title = {AIM 2024 Challenge on Video Saliency Prediction: Methods and Results},
author = {Andrey Moskalenko and Alexey Bryncev and Dmitry Vatolin and Radu Timofte and Gen Zhan and Li Yang and Yunlong Tang and Yiting Liao and Jiongzhi Lin and Baitao Huang and Morteza Moradi and Mohammad Moradi and Francesco Rundo and Concetto Spampinato and Ali Borji and Simone Palazzo and Yuxin Zhu and Yinan Sun and Huiyu Duan and Yuqin Cao and Ziheng Jia and Qiang Hu and Xiongkuo Min and Guangtao Zhai and Hao Fang and Runmin Cong and Xiankai Lu and Xiaofei Zhou and Wei Zhang and Chunyu Zhao and Wentao Mu and Tao Deng and Hamed R. Tavakoli},
journal= {arXiv preprint arXiv:2409.14827},
year = {2024}
}