@inproceedings{437ba7433cdc4660bbeafae93ca26d3c,
title = "A Technical Report for Visual Attention Estimation in HMD Challenge",
abstract = "The 360° video, also known as omnidirectional video (ODV), immersive video or spherical video, has become increasing popular and drawn great research attention. To achieve the effectiveness of 360° videos, it is quite important to understand how human perceive and interact with 360° videos so that efficient techniques for their encoding, transmission, and rendering can be developed. The Visual Attention Estimation in HMD (Head Mounted Display) is a competition to encourage contestants to design lightweight models for predicting human eye attention in 360°videos. The models need to not only achieve high accuracy but also have outstanding performance on HMD devices. We employ the approach of ATSal [1] and fine-tune the expert models with the dataset provided in this event to achieve 0.840 AUC-J, 0.476 CC, 3.206 KLC, 1.478 NSS, 0.412 SIM, currently ranked the 1st place in the qualification competition leaderboard.",
keywords = "360°videos, HMD, Panoramic videos, Video attention estimation",
author = "Chun Tsao and Su, {Po Chyi}",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE; null ; Conference date: 15-11-2021 Through 17-11-2021",
year = "2021",
doi = "10.1109/AIVR52153.2021.00033",
language = "???core.languages.en_GB???",
series = "Proceedings - 2021 4th IEEE International Conference on Artificial Intelligence and Virtual Reality, AIVR 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "143--144",
booktitle = "Proceedings - 2021 4th IEEE International Conference on Artificial Intelligence and Virtual Reality, AIVR 2021",
}