{"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/finding-beans-in-burgers-deep-semantic-visual","title":"Finding beans in burgers: Deep semantic-visual embedding with localization","arxiv_id":"1804.01720","date":"2018-04-05","proceeding":"CVPR 2018 6","authors":["Martin Engilberge","Louis Chevallier","Patrick Pérez","Matthieu Cord"],"abstract":"Several works have proposed to learn a two-path neural network that maps\nimages and texts, respectively, to a same shared Euclidean space where geometry\ncaptures useful semantic relationships. Such a multi-modal embedding can be\ntrained and used for various tasks, notably image captioning. In the present\nwork, we introduce a new architecture of this type, with a visual path that\nleverages recent space-aware pooling mechanisms. Combined with a textual path\nwhich is jointly trained from scratch, our semantic-visual embedding offers a\nversatile model. Once trained under the supervision of captioned images, it\nyields new state-of-the-art performance on cross-modal retrieval. It also\nallows the localization of new concepts from the embedding space into any input\nimage, delivering state-of-the-art result on the visual grounding of phrases.","url_abs":"http://arxiv.org/abs/1804.01720v2","url_pdf":"http://arxiv.org/pdf/1804.01720v2.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":"finding-beans-in-burgers-deep-semantic-visual","repo_url":"https://github.com/technicolor-research/dsve-loc","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"BSD-3-Clause-Clear"}}],"tasks":[{"task_slug":"cross-modal-retrieval","task_name":"Cross-Modal Retrieval"},{"task_slug":"image-captioning","task_name":"Image Captioning"},{"task_slug":"retrieval","task_name":"Retrieval"},{"task_slug":"visual-grounding","task_name":"Visual Grounding"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1804.01720","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}