{"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/memcap-memorizing-style-knowledge-for-image","title":"MemCap: Memorizing Style Knowledge for Image Captioning","arxiv_id":null,"date":"2020-04-03","proceeding":"AAAI 2020 4","authors":["Wentian Zhao","Xinxiao wu","Xiaoxun Zhang"],"abstract":"Generating stylized captions for images is a challenging task since it requires not only describing the content of the image accurately but also expressing the desired linguistic style appropriately. In this paper, we propose MemCap, a novel stylized image captioning method that explicitly encodes the knowledge about linguistic styles with memory mechanism. Rather than relying heavily on a language model to capture style factors in existing methods, our method resorts to memorizing stylized elements learned from training corpus. Particularly, we design a memory module that comprises a set of embedding vectors for encoding style-related phrases in training corpus. To acquire the style-related phrases, we develop a sentence decomposing algorithm that splits a stylized sentence into a style-related part that reflects the linguistic style and a content-related part that contains the visual content. When generating captions, our MemCap first extracts content-relevant style knowledge from the memory module via an attention mechanism and then incorporates the extracted knowledge into a language model. Extensive experiments on two stylized image captioning datasets (SentiCap and FlickrStyle10K) demonstrate the effectiveness of our method.","url_abs":"https://ojs.aaai.org/index.php/AAAI/article/view/6998","url_pdf":"https://ojs.aaai.org/index.php/AAAI/article/view/6998/6852","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":"memcap-memorizing-style-knowledge-for-image","repo_url":"https://github.com/entalent/MemCap","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"image-captioning","task_name":"Image Captioning"},{"task_slug":"language-modeling","task_name":"Language Modeling"},{"task_slug":"language-modelling","task_name":"Language Modelling"},{"task_slug":"sentence","task_name":"Sentence"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-captioning-on-flickrstyle10k","task":"Image Captioning","dataset":"FlickrStyle10K","model":"MemCap","rank_in_archive_order":2,"of":2,"metrics":{"BLEU-1 (Romantic)":"19.9"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}