{"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/captioning-images-with-diverse-objects","title":"Captioning Images with Diverse Objects","arxiv_id":"1606.07770","date":"2016-06-24","proceeding":"CVPR 2017 7","authors":["Subhashini Venugopalan","Lisa Anne Hendricks","Marcus Rohrbach","Raymond Mooney","Trevor Darrell","Kate Saenko"],"abstract":"Recent captioning models are limited in their ability to scale and describe\nconcepts unseen in paired image-text corpora. We propose the Novel Object\nCaptioner (NOC), a deep visual semantic captioning model that can describe a\nlarge number of object categories not present in existing image-caption\ndatasets. Our model takes advantage of external sources -- labeled images from\nobject recognition datasets, and semantic knowledge extracted from unannotated\ntext. We propose minimizing a joint objective which can learn from these\ndiverse data sources and leverage distributional semantic embeddings, enabling\nthe model to generalize and describe novel objects outside of image-caption\ndatasets. We demonstrate that our model exploits semantic information to\ngenerate captions for hundreds of object categories in the ImageNet object\nrecognition dataset that are not observed in MSCOCO image-caption training\ndata, as well as many categories that are observed very rarely. Both automatic\nevaluations and human judgements show that our model considerably outperforms\nprior work in being able to describe many more categories of objects.","url_abs":"http://arxiv.org/abs/1606.07770v3","url_pdf":"http://arxiv.org/pdf/1606.07770v3.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":"captioning-images-with-diverse-objects","repo_url":"https://github.com/willT97/Zero-shot-Image-Captioner","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"object","task_name":"Object"},{"task_slug":"object-recognition","task_name":"Object Recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1606.07770","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1606.07770"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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