{"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/semantically-tied-paired-cycle-consistency","title":"Semantically Tied Paired Cycle Consistency for Zero-Shot Sketch-based Image Retrieval","arxiv_id":"1903.03372","date":"2019-03-08","proceeding":"CVPR 2019 6","authors":["Anjan Dutta","Zeynep Akata"],"abstract":"Zero-shot sketch-based image retrieval (SBIR) is an emerging task in computer\nvision, allowing to retrieve natural images relevant to sketch queries that\nmight not been seen in the training phase. Existing works either require\naligned sketch-image pairs or inefficient memory fusion layer for mapping the\nvisual information to a semantic space. In this work, we propose a semantically\naligned paired cycle-consistent generative (SEM-PCYC) model for zero-shot SBIR,\nwhere each branch maps the visual information to a common semantic space via an\nadversarial training. Each of these branches maintains a cycle consistency that\nonly requires supervision at category levels, and avoids the need of\nhighly-priced aligned sketch-image pairs. A classification criteria on the\ngenerators' outputs ensures the visual to semantic space mapping to be\ndiscriminating. Furthermore, we propose to combine textual and hierarchical\nside information via a feature selection auto-encoder that selects\ndiscriminating side information within a same end-to-end model. Our results\ndemonstrate a significant boost in zero-shot SBIR performance over the\nstate-of-the-art on the challenging Sketchy and TU-Berlin datasets.","url_abs":"http://arxiv.org/abs/1903.03372v1","url_pdf":"http://arxiv.org/pdf/1903.03372v1.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":"semantically-tied-paired-cycle-consistency","repo_url":"https://github.com/AnjanDutta/sem-pcyc","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"image-retrieval","task_name":"Image Retrieval"},{"task_slug":"retrieval","task_name":"Retrieval"},{"task_slug":"sketch-based-image-retrieval","task_name":"Sketch-Based Image Retrieval"},{"task_slug":"feature-selection","task_name":"feature selection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1903.03372","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1903.03372"}},"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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