{"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/vlcap-vision-language-with-contrastive","title":"VLCap: Vision-Language with Contrastive Learning for Coherent Video Paragraph Captioning","arxiv_id":"2206.12972","date":"2022-06-26","proceeding":null,"authors":["Kashu Yamazaki","Sang Truong","Khoa Vo","Michael Kidd","Chase Rainwater","Khoa Luu","Ngan Le"],"abstract":"In this paper, we leverage the human perceiving process, that involves vision and language interaction, to generate a coherent paragraph description of untrimmed videos. We propose vision-language (VL) features consisting of two modalities, i.e., (i) vision modality to capture global visual content of the entire scene and (ii) language modality to extract scene elements description of both human and non-human objects (e.g. animals, vehicles, etc), visual and non-visual elements (e.g. relations, activities, etc). Furthermore, we propose to train our proposed VLCap under a contrastive learning VL loss. The experiments and ablation studies on ActivityNet Captions and YouCookII datasets show that our VLCap outperforms existing SOTA methods on both accuracy and diversity metrics.","url_abs":"https://arxiv.org/abs/2206.12972v2","url_pdf":"https://arxiv.org/pdf/2206.12972v2.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":"vlcap-vision-language-with-contrastive","repo_url":"https://github.com/UARK-AICV/VLCAP","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"contrastive-learning","task_name":"Contrastive Learning"},{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"video-captioning","task_name":"Video Captioning"}],"methods":[{"method_slug":"contrastive-learning","method_name":"Contrastive Learning"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/video-captioning-on-activitynet-captions","task":"Video Captioning","dataset":"ActivityNet Captions","model":"VLCap (ae-test split) - Appearance + Language","rank_in_archive_order":3,"of":5,"metrics":{"BLEU4":"13.38","CIDEr":"31.29","METEOR":"17.48","ROUGE-L":"35.99"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2206.12972","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}