{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/demo2vec-reasoning-object-affordances-from","title":"Demo2Vec: Reasoning Object Affordances From Online Videos","arxiv_id":null,"date":"2018-06-01","proceeding":"CVPR 2018 6","authors":["Kuan Fang","Te-Lin Wu","Daniel Yang","Silvio Savarese","Joseph J. Lim"],"abstract":"Watching expert demonstrations is an important way for humans and robots to reason about affordances of unseen objects. In this paper, we consider the problem of reasoning object affordances through the feature embedding of demonstration videos. We design the Demo2Vec model which learns to extract embedded vectors of demonstration videos and predicts the interaction region and the action label on a target image of the same object. We introduce the Online Product Review dataset for Affordance (OPRA) by collecting and labeling  diverse YouTube product review videos. Our Demo2Vec model outperforms various recurrent neural network baselines on the collected dataset.","url_abs":"http://openaccess.thecvf.com/content_cvpr_2018/html/Fang_Demo2Vec_Reasoning_Object_CVPR_2018_paper.html","url_pdf":"http://openaccess.thecvf.com/content_cvpr_2018/papers/Fang_Demo2Vec_Reasoning_Object_CVPR_2018_paper.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"object","task_name":"Object"},{"task_slug":"video-to-image-affordance-grounding","task_name":"Video-to-image Affordance Grounding"}],"methods":[],"datasets_introduced":[{"slug":"opra","name":"OPRA","full_name":"Online Product Reviews for Affordances"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/video-to-image-affordance-grounding-on-opra","task":"Video-to-image Affordance Grounding","dataset":"OPRA","model":"Demo2Vec","rank_in_archive_order":3,"of":3,"metrics":{"KLD":"2.34","Top-1 Action Accuracy":"40.79"},"uses_additional_data":false},{"leaderboard":"/sota/video-to-image-affordance-grounding-on-opra-1","task":"Video-to-image Affordance Grounding","dataset":"OPRA (28x28)","model":"Demo2Vec","rank_in_archive_order":2,"of":4,"metrics":{"AUC-J":"0.85","KLD":"1.20","SIM":"0.48"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}