{"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/quantifying-the-visual-concreteness-of-words","title":"Quantifying the visual concreteness of words and topics in multimodal datasets","arxiv_id":"1804.06786","date":"2018-04-18","proceeding":"NAACL 2018 6","authors":["Jack Hessel","David Mimno","Lillian Lee"],"abstract":"Multimodal machine learning algorithms aim to learn visual-textual\ncorrespondences. Previous work suggests that concepts with concrete visual\nmanifestations may be easier to learn than concepts with abstract ones. We give\nan algorithm for automatically computing the visual concreteness of words and\ntopics within multimodal datasets. We apply the approach in four settings,\nranging from image captions to images/text scraped from historical books. In\naddition to enabling explorations of concepts in multimodal datasets, our\nconcreteness scores predict the capacity of machine learning algorithms to\nlearn textual/visual relationships. We find that 1) concrete concepts are\nindeed easier to learn; 2) the large number of algorithms we consider have\nsimilar failure cases; 3) the precise positive relationship between\nconcreteness and performance varies between datasets. We conclude with\nrecommendations for using concreteness scores to facilitate future multimodal\nresearch.","url_abs":"http://arxiv.org/abs/1804.06786v2","url_pdf":"http://arxiv.org/pdf/1804.06786v2.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":"quantifying-the-visual-concreteness-of-words","repo_url":"https://github.com/victorssilva/concreteness","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"image-captioning","task_name":"Image Captioning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1804.06786","atlas_url":"https://app.syntology.ai/?focus=1804.06786","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}