{"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/aligning-where-to-see-and-what-to-tell-image","title":"Aligning where to see and what to tell: image caption with region-based attention and scene factorization","arxiv_id":"1506.06272","date":"2015-06-20","proceeding":null,"authors":["Junqi Jin","Kun fu","Runpeng Cui","Fei Sha","Chang-Shui Zhang"],"abstract":"Recent progress on automatic generation of image captions has shown that it\nis possible to describe the most salient information conveyed by images with\naccurate and meaningful sentences. In this paper, we propose an image caption\nsystem that exploits the parallel structures between images and sentences. In\nour model, the process of generating the next word, given the previously\ngenerated ones, is aligned with the visual perception experience where the\nattention shifting among the visual regions imposes a thread of visual\nordering. This alignment characterizes the flow of \"abstract meaning\", encoding\nwhat is semantically shared by both the visual scene and the text description.\nOur system also makes another novel modeling contribution by introducing\nscene-specific contexts that capture higher-level semantic information encoded\nin an image. The contexts adapt language models for word generation to specific\nscene types. We benchmark our system and contrast to published results on\nseveral popular datasets. We show that using either region-based attention or\nscene-specific contexts improves systems without those components. Furthermore,\ncombining these two modeling ingredients attains the state-of-the-art\nperformance.","url_abs":"http://arxiv.org/abs/1506.06272v1","url_pdf":"http://arxiv.org/pdf/1506.06272v1.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":"aligning-where-to-see-and-what-to-tell-image","repo_url":"https://github.com/fukun07/neural-image-captioning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"image-captioning","task_name":"Image Captioning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}