{"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/generating-diverse-and-accurate-visual","title":"Generating Diverse and Accurate Visual Captions by Comparative Adversarial Learning","arxiv_id":"1804.00861","date":"2018-04-03","proceeding":null,"authors":["Dianqi Li","Qiuyuan Huang","Xiaodong He","Lei Zhang","Ming-Ting Sun"],"abstract":"We study how to generate captions that are not only accurate in describing an\nimage but also discriminative across different images. The problem is both\nfundamental and interesting, as most machine-generated captions, despite\nphenomenal research progresses in the past several years, are expressed in a\nvery monotonic and featureless format. While such captions are normally\naccurate, they often lack important characteristics in human languages -\ndistinctiveness for each caption and diversity for different images. To address\nthis problem, we propose a novel conditional generative adversarial network for\ngenerating diverse captions across images. Instead of estimating the quality of\na caption solely on one image, the proposed comparative adversarial learning\nframework better assesses the quality of captions by comparing a set of\ncaptions within the image-caption joint space. By contrasting with\nhuman-written captions and image-mismatched captions, the caption generator\neffectively exploits the inherent characteristics of human languages, and\ngenerates more discriminative captions. We show that our proposed network is\ncapable of producing accurate and diverse captions across images.","url_abs":"http://arxiv.org/abs/1804.00861v3","url_pdf":"http://arxiv.org/pdf/1804.00861v3.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":"generating-diverse-and-accurate-visual","repo_url":"https://github.com/Anjaney1999/image-captioning-seqgan","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":null,"task_name":"Generative Adversarial Network"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1804.00861","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.00861"}},"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. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/Anjaney1999/image-captioning-seqgan","reach":null}],"summary":{"unverified":1},"by_repo_kind":{},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":1,"samples":[{"code_sha256_prefix":"22225c242e4c65f6","entry":"sample_from_start","repo":null,"repo_kind":null,"path":null,"file_url":null,"link_basis":"identical_code_first_harvested_elsewhere","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":null,"inline_ok":false,"mcp_get_code":{"code_sha256":"22225c242e4c65f6"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}