{"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/reconstruction-network-for-video-captioning","title":"Reconstruction Network for Video Captioning","arxiv_id":"1803.11438","date":"2018-03-30","proceeding":"CVPR 2018 6","authors":["Bairui Wang","Lin Ma","Wei zhang","Wei Liu"],"abstract":"In this paper, the problem of describing visual contents of a video sequence\nwith natural language is addressed. Unlike previous video captioning work\nmainly exploiting the cues of video contents to make a language description, we\npropose a reconstruction network (RecNet) with a novel\nencoder-decoder-reconstructor architecture, which leverages both the forward\n(video to sentence) and backward (sentence to video) flows for video\ncaptioning. Specifically, the encoder-decoder makes use of the forward flow to\nproduce the sentence description based on the encoded video semantic features.\nTwo types of reconstructors are customized to employ the backward flow and\nreproduce the video features based on the hidden state sequence generated by\nthe decoder. The generation loss yielded by the encoder-decoder and the\nreconstruction loss introduced by the reconstructor are jointly drawn into\ntraining the proposed RecNet in an end-to-end fashion. Experimental results on\nbenchmark datasets demonstrate that the proposed reconstructor can boost the\nencoder-decoder models and leads to significant gains in video caption\naccuracy.","url_abs":"http://arxiv.org/abs/1803.11438v1","url_pdf":"http://arxiv.org/pdf/1803.11438v1.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":"reconstruction-network-for-video-captioning","repo_url":"https://github.com/chldydgh4687/2020-1.VideoCaptioning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"reconstruction-network-for-video-captioning","repo_url":"https://github.com/hobincar/RecNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"reconstruction-network-for-video-captioning","repo_url":"https://github.com/nasib-ullah/video-captioning-models-in-Pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"video-captioning","task_name":"Video Captioning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1803.11438","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1803.11438"}},"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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