30支队伍用鼠标追踪数据预测视频显著性，7队提交最终结果。
AIM 2024 Challenge on Video Saliency Prediction: Methods and Results
- 基于众包鼠标轨迹构建大规模音视频显著性数据集。
- 7支队伍在私有测试集上表现最佳，指标优于基准方法。
- 适合关注视频注意力建模与人眼感知的研究者参考。
本文回顾了2024年AIM视频显著性预测挑战赛。参赛者需为提供的视频序列生成准确的显著性图，该任务在视频压缩、质量评估及广告等领域具有广泛应用。比赛采用全新构建的大规模音视频鼠迹显著性数据集（AViMoS），包含1500个视频，每个视频有超过70名观察者参与，通过众包鼠标追踪采集。该数据集已通过传统眼动追踪数据验证，一致性高。共有30支团队注册，7支团队完成最终阶段提交。最终方案在私有测试子集上使用常用质量指标进行评测并排名。本报告呈现了评估结果及各方案描述。所有数据，包括私有测试集，均已公开于挑战赛主页：https://challenges.videoprocessing.ai/challenges/video-saliency-prediction.html。
原文摘要 · Abstract (English)
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.
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