Detecting attended visual targets in video の勉強会用資料Yasunori Ozaki
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第三回 全日本コンピュータビジョン勉強会(後編)で発表した Detecting attended visual targets in video のまとめ資料です。映像中にいる人物が注意を払っている対象を推定するタスクを解いた話です。コンピュータビジョンや認知科学などに興味がある方はぜひご覧ください。
Detecting attended visual targets in video の勉強会用資料Yasunori Ozaki
?
第三回 全日本コンピュータビジョン勉強会(後編)で発表した Detecting attended visual targets in video のまとめ資料です。映像中にいる人物が注意を払っている対象を推定するタスクを解いた話です。コンピュータビジョンや認知科学などに興味がある方はぜひご覧ください。
Selection of housing, one of the necessities of human life, has a great influence on life for a long time. However, since it requires a wide range of information gathering and consideration before decision, state-of-the-art recommendation algorithms such as collaborative filtering do not work well. In this presentation, after reviewing issues specific to the real estate field, I cited examples of "application of crowdsourcing to social media (Twitter timelines)" and "application of deep learning to property images" as an effort by our research group. Finally I discuss what kind of AI technology is applicable in the real estate field.
Mining User Experience through Crowdsourcing: A Property Search Behavior Corp...Yoji Kiyota
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This document describes a study that aimed to establish a method for understanding user experiences in property searching through analyzing Twitter timelines. The researchers collected Twitter timelines of followers of a Japanese property search service account and used crowdsourcing microtasks to extract tweets related to property searching and analyze them based on a conventional property search process framework. Workers were asked to categorize timeline fragments as either related or unrelated to property searching. This allowed the researchers to build a corpus of property search behavior data derived from social media for analyzing user needs and experiences.