{"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/joint-event-detection-and-description-in","title":"Joint Event Detection and Description in Continuous Video Streams","arxiv_id":"1802.10250","date":"2018-02-28","proceeding":null,"authors":["Huijuan Xu","Boyang Li","Vasili Ramanishka","Leonid Sigal","Kate Saenko"],"abstract":"Dense video captioning is a fine-grained video understanding task that\ninvolves two sub-problems: localizing distinct events in a long video stream,\nand generating captions for the localized events. We propose the Joint Event\nDetection and Description Network (JEDDi-Net), which solves the dense video\ncaptioning task in an end-to-end fashion. Our model continuously encodes the\ninput video stream with three-dimensional convolutional layers, proposes\nvariable-length temporal events based on pooled features, and generates their\ncaptions. Proposal features are extracted within each proposal segment through\n3D Segment-of-Interest pooling from shared video feature encoding. In order to\nexplicitly model temporal relationships between visual events and their\ncaptions in a single video, we also propose a two-level hierarchical captioning\nmodule that keeps track of context. On the large-scale ActivityNet Captions\ndataset, JEDDi-Net demonstrates improved results as measured by standard\nmetrics. We also present the first dense captioning results on the\nTACoS-MultiLevel dataset.","url_abs":"http://arxiv.org/abs/1802.10250v3","url_pdf":"http://arxiv.org/pdf/1802.10250v3.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":"joint-event-detection-and-description-in","repo_url":"https://github.com/VisionLearningGroup/JEDDi-Net","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"dense-captioning","task_name":"Dense Captioning"},{"task_slug":"dense-video-captioning","task_name":"Dense Video Captioning"},{"task_slug":"event-detection","task_name":"Event Detection"},{"task_slug":"video-captioning","task_name":"Video Captioning"},{"task_slug":"video-understanding","task_name":"Video Understanding"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1802.10250","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1802.10250"}},"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/VisionLearningGroup/JEDDi-Net","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"unverified":1},"by_repo_kind":{"official":{"samples":1,"ran":0,"repositories":1}},"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":0,"samples":[{"code_sha256_prefix":"0087afa9a6463790","entry":"py_cpu_nms","repo":"VisionLearningGroup/JEDDi-Net","repo_kind":"official","path":"lib/nms/py_cpu_nms.py","file_url":"https://github.com/VisionLearningGroup/JEDDi-Net/blob/HEAD/lib/nms/py_cpu_nms.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"0087afa9a6463790"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}