{"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/sme-net-sparse-motion-estimation-for","title":"SME-Net: Sparse Motion Estimation for Parametric Video Prediction Through Reinforcement Learning","arxiv_id":null,"date":"2019-10-01","proceeding":"ICCV 2019 10","authors":["Yung-Han Ho"," Chuan-Yuan Cho"," Wen-Hsiao Peng"," Guo-Lun Jin"],"abstract":"This paper leverages a classic prediction technique, known as parametric overlapped block motion compensation (POBMC), in a reinforcement learning framework for video prediction. Learning-based prediction methods with explicit motion models often suffer from having to estimate large numbers of motion parameters with artificial regularization. Inspired by the success of sparse motion-based prediction for video compression, we propose a parametric video prediction on a sparse motion field composed of few critical pixels and their motion vectors. The prediction is achieved by gradually refining the estimate of a future frame in iterative, discrete steps. Along the way, the identification of critical pixels and their motion estimation are addressed by two neural networks trained under a reinforcement learning setting. Our model achieves the state-of-the-art performance on CaltchPed, UCF101 and CIF datasets in one-step and multi-step prediction tests. It shows good generalization results and is able to learn well on small training data. \r","url_abs":"http://openaccess.thecvf.com/content_ICCV_2019/html/Ho_SME-Net_Sparse_Motion_Estimation_for_Parametric_Video_Prediction_Through_Reinforcement_ICCV_2019_paper.html","url_pdf":"http://openaccess.thecvf.com/content_ICCV_2019/papers/Ho_SME-Net_Sparse_Motion_Estimation_for_Parametric_Video_Prediction_Through_Reinforcement_ICCV_2019_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":[{"paper_slug":"sme-net-sparse-motion-estimation-for","repo_url":"https://github.com/MindSpore-scientific-2/code-11/tree/main/SmeLU-master","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"sme-net-sparse-motion-estimation-for","repo_url":"https://github.com/MindSpore-scientific-2/code-2/tree/main/SmeLU-master","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"sme-net-sparse-motion-estimation-for","repo_url":"https://github.com/MindSpore-scientific-2/code-8/tree/main/SmeLU-master","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"sme-net-sparse-motion-estimation-for","repo_url":"https://github.com/hectorho0409/SME_release","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"motion-compensation","task_name":"Motion Compensation"},{"task_slug":"motion-estimation","task_name":"Motion Estimation"},{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"},{"task_slug":"video-compression","task_name":"Video Compression"},{"task_slug":"video-prediction","task_name":"Video Prediction"},{"task_slug":"reinforcement-learning-2","task_name":"reinforcement-learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}