{"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/object-level-visual-reasoning-in-videos","title":"Object Level Visual Reasoning in Videos","arxiv_id":"1806.06157","date":"2018-06-16","proceeding":"ECCV 2018 9","authors":["Fabien Baradel","Natalia Neverova","Christian Wolf","Julien Mille","Greg Mori"],"abstract":"Human activity recognition is typically addressed by detecting key concepts\nlike global and local motion, features related to object classes present in the\nscene, as well as features related to the global context. The next open\nchallenges in activity recognition require a level of understanding that pushes\nbeyond this and call for models with capabilities for fine distinction and\ndetailed comprehension of interactions between actors and objects in a scene.\nWe propose a model capable of learning to reason about semantically meaningful\nspatiotemporal interactions in videos. The key to our approach is a choice of\nperforming this reasoning at the object level through the integration of state\nof the art object detection networks. This allows the model to learn detailed\nspatial interactions that exist at a semantic, object-interaction relevant\nlevel. We evaluate our method on three standard datasets (Twenty-BN\nSomething-Something, VLOG and EPIC Kitchens) and achieve state of the art\nresults on all of them. Finally, we show visualizations of the interactions\nlearned by the model, which illustrate object classes and their interactions\ncorresponding to different activity classes.","url_abs":"http://arxiv.org/abs/1806.06157v3","url_pdf":"http://arxiv.org/pdf/1806.06157v3.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":[{"paper_slug":"object-level-visual-reasoning-in-videos","repo_url":"https://github.com/fabienbaradel/object_level_visual_reasoning","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"activity-recognition","task_name":"Activity Recognition"},{"task_slug":"human-activity-recognition","task_name":"Human Activity Recognition"},{"task_slug":"object","task_name":"Object"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"visual-reasoning","task_name":"Visual Reasoning"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1806.06157","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}