{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T16:46:08Z","timestamp":1779295568549,"version":"3.51.4"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,8]]},"abstract":"<jats:p>This paper addresses the problem of inferring 3D human attention in RGB-D videos at scene scale. 3D human attention describes where a human is looking in 3D scenes. We propose a probabilistic method to jointly model attention, intentions, and their interactions. Latent intentions guide human attention which conversely reveals the intention features. This mutual interaction makes attention inference a joint optimization with latent intentions. An EM-based approach is adopted to learn the latent intentions and model parameters. Given an RGB-D video with 3D human skeletons, a joint-state dynamic programming algorithm is utilized to jointly infer the latent intentions, the 3D attention directions, and the attention voxels in scene point clouds. Experiments on a new 3D human attention dataset prove the strength of our method.<\/jats:p>","DOI":"10.24963\/ijcai.2017\/180","type":"proceedings-article","created":{"date-parts":[[2017,7,28]],"date-time":"2017-07-28T09:14:07Z","timestamp":1501233247000},"page":"1297-1303","source":"Crossref","is-referenced-by-count":14,"title":["Inferring Human Attention by Learning Latent Intentions"],"prefix":"10.24963","author":[{"given":"Ping","family":"Wei","sequence":"first","affiliation":[{"name":"Xi'an Jiaotong University, Xi'an, China"},{"name":"University of California, Los Angeles, Los Angeles, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dan","family":"Xie","sequence":"additional","affiliation":[{"name":"University of California, Los Angeles, Los Angeles, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nanning","family":"Zheng","sequence":"additional","affiliation":[{"name":"Xi'an Jiaotong University, Xi'an, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Song-Chun","family":"Zhu","sequence":"additional","affiliation":[{"name":"University of California, Los Angeles, Los Angeles, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"name":"Twenty-Sixth International Joint Conference on Artificial Intelligence","theme":"Artificial Intelligence","location":"Melbourne, Australia","acronym":"IJCAI-2017","number":"26","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)","University of Technology Sydney (UTS)","Australian Computer Society (ACS)"],"start":{"date-parts":[[2017,8,19]]},"end":{"date-parts":[[2017,8,26]]}},"container-title":["Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2017,7,28]],"date-time":"2017-07-28T11:52:38Z","timestamp":1501242758000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2017\/180"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2017,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2017\/180","relation":{},"subject":[],"published":{"date-parts":[[2017,8]]}}}