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Contrastive Learning (CL) is one of the most well-known approaches in SSL that attempts to learn general, informative representations of data. CL methods have been mostly developed for applications in computer vision and natural language processing where only a single sensor modality is used. A majority of pervasive computing applications, however, exploit data from a range of different sensor modalities. While existing CL methods are limited to learning from one or two data sources, we propose COCOA (Cross mOdality COntrastive leArning), a self-supervised model that employs a novel objective function to learn quality representations from multisensor data by computing the cross-correlation between different data modalities and minimizing the similarity between irrelevant instances. We evaluate the effectiveness of COCOA against eight recently introduced state-of-the-art self-supervised models, and two supervised baselines across five public datasets. We show that COCOA achieves superior classification performance to all other approaches. Also, COCOA is far more label-efficient than the other baselines including the fully supervised model using only one-tenth of available labeled data.<\/jats:p>","DOI":"10.1145\/3550316","type":"journal-article","created":{"date-parts":[[2022,9,7]],"date-time":"2022-09-07T14:54:27Z","timestamp":1662562467000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":80,"title":["COCOA"],"prefix":"10.1145","volume":"6","author":[{"given":"Shohreh","family":"Deldari","sequence":"first","affiliation":[{"name":"School of Computing and Technologies, RMIT University, Melbourne, Victoria, Australia"}]},{"given":"Hao","family":"Xue","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, University of New South Wales, Sydeny, NSW, Australia"}]},{"given":"Aaqib","family":"Saeed","sequence":"additional","affiliation":[{"name":"Philips Research, Eindhoven, Netherlands"}]},{"given":"Daniel V.","family":"Smith","sequence":"additional","affiliation":[{"name":"Data61, CSIRO, Hobart, Tasmania, Australia"}]},{"given":"Flora D.","family":"Salim","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia"}]}],"member":"320","published-online":{"date-parts":[[2022,9,7]]},"reference":[{"key":"e_1_2_2_1_1","volume-title":"Vatt: Transformers for multimodal self-supervised learning from raw video, audio and text. 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