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Given videos as sequences of frames and queries as sequences of\nwords, an effective sequence-to-sequence cross-modal matching is required. The\nmajority of existing methods are concept based, extracting relevant concepts\nfrom queries and videos and accordingly establishing associations between the\ntwo modalities. In contrast, this paper takes a concept-free approach,\nproposing a dual deep encoding network that encodes videos and queries into\npowerful dense representations of their own. Dual encoding is conceptually\nsimple, practically effective and end-to-end. As experiments on three\nbenchmarks, i.e. MSR-VTT, TRECVID 2016 and 2017 Ad-hoc Video Search show, the\nproposed solution establishes a new state-of-the-art for zero-example video\nretrieval.","url_abs":"http://arxiv.org/abs/1809.06181v3","url_pdf":"http://arxiv.org/pdf/1809.06181v3.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":"dual-dense-encoding-for-zero-example-video","repo_url":"https://github.com/danieljf24/dual_encoding","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"ad-hoc-video-search","task_name":"Ad-hoc video search"},{"task_slug":"retrieval","task_name":"Retrieval"},{"task_slug":"video-retrieval","task_name":"Video Retrieval"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1809.06181","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1809.06181"}},"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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