@techreport{oai:ipsj.ixsq.nii.ac.jp:00216970,
 author = {于, 澤程 and 黄, 逸飛 and 古田, 諒佑 and 郷津, 優介 and 佐藤, 洋一 and Zecheng, Yu and Yifei, Huang and Ryosuke, Furuta and Yusuke, Goutsu and Yoichi, Sato},
 issue = {39},
 month = {Mar},
 note = {Object affordance has attracted a growing interest in computer vision. It is an important concept that builds a bridge between human ability and object property, and provides fine-grained information for other tasks like activity forecasting, scene understanding, etc. Although affordance is investigated in many previous works, existing affordance datasets failed to distinguish affordance from other concepts like action and function. In this paper, We propose an efficient affordance annotation scheme for egocentric action video datasets to address this issue, which gives a clear and accurate definition of affordance. The scheme was applied to two large-scale egocentric video datasets: EPIC-KITCHENS and HOMAGE, and tested with various benchmark tasks., Object affordance has attracted a growing interest in computer vision. It is an important concept that builds a bridge between human ability and object property, and provides fine-grained information for other tasks like activity forecasting, scene understanding, etc. Although affordance is investigated in many previous works, existing affordance datasets failed to distinguish affordance from other concepts like action and function. In this paper, We propose an efficient affordance annotation scheme for egocentric action video datasets to address this issue, which gives a clear and accurate definition of affordance. The scheme was applied to two large-scale egocentric video datasets: EPIC-KITCHENS and HOMAGE, and tested with various benchmark tasks.},
 title = {自己視点動作映像に対する厳密なアフォーダンスのアノテーション},
 year = {2022}
}