{"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/deep-captioning-with-multimodal-recurrent","title":"Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN)","arxiv_id":"1412.6632","date":"2014-12-20","proceeding":null,"authors":["Junhua Mao","Wei Xu","Yi Yang","Jiang Wang","Zhiheng Huang","Alan Yuille"],"abstract":"In this paper, we present a multimodal Recurrent Neural Network (m-RNN) model\nfor generating novel image captions. It directly models the probability\ndistribution of generating a word given previous words and an image. Image\ncaptions are generated by sampling from this distribution. The model consists\nof two sub-networks: a deep recurrent neural network for sentences and a deep\nconvolutional network for images. These two sub-networks interact with each\nother in a multimodal layer to form the whole m-RNN model. The effectiveness of\nour model is validated on four benchmark datasets (IAPR TC-12, Flickr 8K,\nFlickr 30K and MS COCO). Our model outperforms the state-of-the-art methods. In\naddition, we apply the m-RNN model to retrieval tasks for retrieving images or\nsentences, and achieves significant performance improvement over the\nstate-of-the-art methods which directly optimize the ranking objective function\nfor retrieval. The project page of this work is:\nwww.stat.ucla.edu/~junhua.mao/m-RNN.html .","url_abs":"http://arxiv.org/abs/1412.6632v5","url_pdf":"http://arxiv.org/pdf/1412.6632v5.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":"deep-captioning-with-multimodal-recurrent","repo_url":"https://github.com/mjhucla/mRNN-CR","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"deep-captioning-with-multimodal-recurrent","repo_url":"https://github.com/mjhucla/TF-mRNN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":null,"task_name":"8k"},{"task_slug":"image-captioning","task_name":"Image Captioning"},{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1412.6632","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1412.6632"}},"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/mjhucla/mRNN-CR","reach":{"status":"unanswered"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/mjhucla/TF-mRNN","reach":null}],"summary":{"ran_honours":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":"0e80fb39f95dde2a","entry":"run_epoch","repo":null,"repo_kind":null,"path":null,"file_url":null,"link_basis":"identical_code_first_harvested_elsewhere","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"mcp_get_code":{"code_sha256":"0e80fb39f95dde2a"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}