{"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/densenet-for-dense-flow","title":"DenseNet for Dense Flow","arxiv_id":"1707.06316","date":"2017-07-19","proceeding":null,"authors":["Yi Zhu","Shawn Newsam"],"abstract":"Classical approaches for estimating optical flow have achieved rapid progress\nin the last decade. However, most of them are too slow to be applied in\nreal-time video analysis. Due to the great success of deep learning, recent\nwork has focused on using CNNs to solve such dense prediction problems. In this\npaper, we investigate a new deep architecture, Densely Connected Convolutional\nNetworks (DenseNet), to learn optical flow. This specific architecture is ideal\nfor the problem at hand as it provides shortcut connections throughout the\nnetwork, which leads to implicit deep supervision. We extend current DenseNet\nto a fully convolutional network to learn motion estimation in an unsupervised\nmanner. Evaluation results on three standard benchmarks demonstrate that\nDenseNet is a better fit than other widely adopted CNN architectures for\noptical flow estimation.","url_abs":"http://arxiv.org/abs/1707.06316v1","url_pdf":"http://arxiv.org/pdf/1707.06316v1.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":"densenet-for-dense-flow","repo_url":"https://github.com/Mind23-2/MindCode-4/tree/main/densenet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[{"task_slug":"motion-estimation","task_name":"Motion Estimation"},{"task_slug":"optical-flow-estimation","task_name":"Optical Flow Estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}