{"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/unsupervised-video-domain-adaptation-with","title":"Unsupervised Video Domain Adaptation with Masked Pre-Training and Collaborative Self-Training","arxiv_id":"2312.02914","date":"2023-12-05","proceeding":"CVPR 2024 1","authors":["Arun Reddy","William Paul","Corban Rivera","Ketul Shah","Celso M. de Melo","Rama Chellappa"],"abstract":"In this work, we tackle the problem of unsupervised domain adaptation (UDA) for video action recognition. Our approach, which we call UNITE, uses an image teacher model to adapt a video student model to the target domain. UNITE first employs self-supervised pre-training to promote discriminative feature learning on target domain videos using a teacher-guided masked distillation objective. We then perform self-training on masked target data, using the video student model and image teacher model together to generate improved pseudolabels for unlabeled target videos. Our self-training process successfully leverages the strengths of both models to achieve strong transfer performance across domains. We evaluate our approach on multiple video domain adaptation benchmarks and observe significant improvements upon previously reported results.","url_abs":"https://arxiv.org/abs/2312.02914v4","url_pdf":"https://arxiv.org/pdf/2312.02914v4.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":"unsupervised-video-domain-adaptation-with","repo_url":"https://github.com/reddyav1/unite","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"action-recognition-in-videos","task_name":"Action Recognition"},{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"},{"task_slug":"action-recognition","task_name":"Temporal Action Localization"},{"task_slug":"unsupervised-domain-adaptation","task_name":"Unsupervised Domain Adaptation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/domain-adaptation-on-hmdb-ucf-full","task":"Domain Adaptation","dataset":"HMDB --> UCF (full)","model":"UNITE","rank_in_archive_order":2,"of":4,"metrics":{"Accuracy":"92.5"},"uses_additional_data":false},{"leaderboard":"/sota/domain-adaptation-on-hmdbfull-to-ucf","task":"Domain Adaptation","dataset":"HMDBfull-to-UCF","model":"UNITE","rank_in_archive_order":1,"of":5,"metrics":{"Accuracy":"92.5"},"uses_additional_data":false},{"leaderboard":"/sota/domain-adaptation-on-ucf-hmdb-full","task":"Domain Adaptation","dataset":"UCF --> HMDB (full)","model":"UNITE","rank_in_archive_order":1,"of":5,"metrics":{"Accuracy":"95.0"},"uses_additional_data":false},{"leaderboard":"/sota/domain-adaptation-on-ucf-to-hmdbfull","task":"Domain Adaptation","dataset":"UCF-to-HMDBfull","model":"UNITE","rank_in_archive_order":1,"of":5,"metrics":{"Accuracy":"95.0"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2312.02914","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}