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Related prior works either require aligned sketch-image pairs that are costly to obtain or inefficient memory fusion layer for mapping the visual information to a semantic space. In this paper, we address any-shot,<jats:italic>i.e.<\/jats:italic>\u00a0zero-shot and few-shot, sketch-based image retrieval (SBIR) tasks, where we introduce the few-shot setting for SBIR. For solving these tasks, we propose a semantically aligned paired cycle-consistent generative adversarial network (SEM-PCYC) for any-shot SBIR, where each branch of the generative adversarial network maps the visual information from sketch and image to a common semantic space via adversarial training. Each of these branches maintains cycle consistency that only requires supervision at the category level, and avoids the need of aligned sketch-image pairs. A classification criteria on the generators\u2019 outputs ensures the visual to semantic space mapping to be class-specific. Furthermore, we propose to combine textual and hierarchical side information via an auto-encoder that selects discriminating side information within a same end-to-end model. Our results demonstrate a significant boost in any-shot SBIR performance over the state-of-the-art on the extended version of the challenging Sketchy, TU-Berlin and QuickDraw datasets.<\/jats:p>","DOI":"10.1007\/s11263-020-01350-x","type":"journal-article","created":{"date-parts":[[2020,7,29]],"date-time":"2020-07-29T06:03:44Z","timestamp":1596002624000},"page":"2684-2703","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Semantically Tied Paired Cycle Consistency for Any-Shot Sketch-Based Image Retrieval"],"prefix":"10.1007","volume":"128","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1667-2245","authenticated-orcid":false,"given":"Anjan","family":"Dutta","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zeynep","family":"Akata","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,7,29]]},"reference":[{"key":"1350_CR1","doi-asserted-by":"crossref","unstructured":"Akata, Z., Malinowski, M., Fritz, M., & Schiele, B. 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