Towards Efficient Visual Attention Prediction for 360 Degree Videos
2024 IEEE International Conference on Artificial Intelligence and eXtended and Virtual Reality (AIxVR)(2024)
摘要
Visual attention prediction refers to the ability to predict the most visually important or attention-grabbing areas in a scene, and emphasize them to create an engaging and realistic experience for the user. These technologies require real-time processing of high-quality visual content to maintain user engagement and immersion. As such, it is necessary to use lightweight models that can predict the most important regions of a scene without incurring large computational cost. The contribution of this work is the development and evaluation of a lightweight model for visual attention prediction, which serves as a baseline on public datasets. We study various model design choices and their effects on the performance and efficiency. We also study the effect of a model compression technique, namely self-distillation.
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关键词
visual attention,saliency prediction,360 degree videos,edge devices
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