{"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/learning-joint-embedding-with-multimodal-cues","title":"Learning Joint Embedding with Multimodal Cues for Cross-Modal Video-Text Retrieval","arxiv_id":null,"date":"2018-06-11","proceeding":"ICMR 2018 6","authors":["Niluthpol Chowdhury Mithun","Juncheng Li","Florian Metze","Amit K. Roy-Chowdhury"],"abstract":"Constructing a joint representation invariant across different modalities (e.g., video, language) is of significant importance in many multimedia applications. While there are a number of recent successes in developing effective image-text retrieval methods by learning\r\njoint representations, the video-text retrieval task, in contrast, has not been explored to its fullest extent. In this paper, we study how\r\nto effectively utilize available multi-modal cues from videos for the cross-modal video-text retrieval task. Based on our analysis,\r\nwe propose a novel framework that simultaneously utilizes multimodal features (different visual characteristics, audio inputs, and text) by a fusion strategy for efficient retrieval. Furthermore, we explore several loss functions in training the joint embedding and propose a modified pairwise ranking loss for the retrieval task. Experiments on MSVD and MSR-VTT datasets demonstrate that our method achieves significant performance gain compared to the state-of-the-art approaches.","url_abs":"https://dl.acm.org/citation.cfm?id=3206064","url_pdf":"http://www.cs.cmu.edu/~fmetze/interACT/Publications_files/publications/ICMR2018_Camera_Ready.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":"learning-joint-embedding-with-multimodal-cues","repo_url":"https://github.com/niluthpol/multimodal_vtt","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"image-text-retrieval","task_name":"Image-text Retrieval"},{"task_slug":"retrieval","task_name":"Retrieval"},{"task_slug":"text-retrieval","task_name":"Text Retrieval"},{"task_slug":"video-retrieval","task_name":"Video Retrieval"},{"task_slug":"video-text-retrieval","task_name":"Video-Text Retrieval"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/video-retrieval-on-msr-vtt","task":"Video Retrieval","dataset":"MSR-VTT","model":"JEMC","rank_in_archive_order":38,"of":40,"metrics":{"text-to-video Mean Rank":"213.8","text-to-video Median Rank":"29.7","text-to-video R@1":"7.0","text-to-video R@10":"29.7","text-to-video R@5":"20.9","video-to-text Mean Rank":"134","video-to-text Median Rank":"16","video-to-text R@1":"12.5","video-to-text R@10":"42.2","video-to-text R@5":"32.1"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}