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To achieve this sign spotting task, we train a model using multiple types of available supervision by: (1)<jats:italic>watching<\/jats:italic>existing footage which is sparsely labelled using mouthing cues; (2)<jats:italic>reading<\/jats:italic>associated subtitles (readily available translations of the signed content) which provide additional<jats:italic>weak-supervision<\/jats:italic>; (3)<jats:italic>looking up<\/jats:italic>words (for which no co-articulated labelled examples are available) in visual sign language dictionaries to enable novel sign spotting. These three tasks are integrated into a unified learning framework using the principles of Noise Contrastive Estimation and Multiple Instance Learning. We validate the effectiveness of our approach on low-shot sign spotting benchmarks. In addition, we contribute a machine-readable British Sign Language (BSL) dictionary dataset of isolated signs,<jats:sc>BslDict<\/jats:sc>, to facilitate study of this task. The dataset, models and code are available at our project page.<\/jats:p>","DOI":"10.1007\/s11263-022-01589-6","type":"journal-article","created":{"date-parts":[[2022,4,5]],"date-time":"2022-04-05T06:02:49Z","timestamp":1649138569000},"page":"1416-1439","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Scaling Up Sign Spotting Through Sign Language Dictionaries"],"prefix":"10.1007","volume":"130","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8438-6152","authenticated-orcid":false,"given":"G\u00fcl","family":"Varol","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liliane","family":"Momeni","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Samuel","family":"Albanie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Triantafyllos","family":"Afouras","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrew","family":"Zisserman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,4,5]]},"reference":[{"key":"1589_CR1","unstructured":"Afouras, T., Chung, J.S., & Zisserman, A. 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