{"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/classify-predict-detect-anticipate-and","title":"A Probabilistic Semi-Supervised Approach to Multi-Task Human Activity Modeling","arxiv_id":"1809.08875","date":"2018-09-24","proceeding":null,"authors":["Judith Bütepage","Hedvig Kjellström","Danica Kragic"],"abstract":"Human behavior is a continuous stochastic spatio-temporal process which is\ngoverned by semantic actions and affordances as well as latent factors.\nTherefore, video-based human activity modeling is concerned with a number of\ntasks such as inferring current and future semantic labels, predicting future\ncontinuous observations as well as imagining possible future label and feature\nsequences. In this paper we present a semi-supervised probabilistic deep latent\nvariable model that can represent both discrete labels and continuous\nobservations as well as latent dynamics over time. This allows the model to\nsolve several tasks at once without explicit fine-tuning. We focus here on the\ntasks of action classification, detection, prediction and anticipation as well\nas motion prediction and synthesis based on 3D human activity data recorded\nwith Kinect. We further extend the model to capture hierarchical label\nstructure and to model the dependencies between multiple entities, such as a\nhuman and objects. Our experiments demonstrate that our principled approach to\nhuman activity modeling can be used to detect current and anticipate future\nsemantic labels and to predict and synthesize future label and feature\nsequences. When comparing our model to state-of-the-art approaches, which are\nspecifically designed for e.g. action classification, we find that our\nprobabilistic formulation outperforms or is comparable to these task specific\nmodels.","url_abs":"http://arxiv.org/abs/1809.08875v3","url_pdf":"http://arxiv.org/pdf/1809.08875v3.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":"classify-predict-detect-anticipate-and","repo_url":"https://github.com/jbutepage/semi_supervised_variational_recurrent_neural_network","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"action-classification","task_name":"Action Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"trajectory-prediction","task_name":"Trajectory Prediction"},{"task_slug":"motion-prediction","task_name":"motion prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}