[29] Shuang Ma, Sai Vemprala, Wenshan Wang, Jayesh K. Gupta, Yale Song, Daniel McDufft, and Ashish Kapoor. Compass: Contrastive multimodal pretraining for autonomous systems. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022.
[30] Hiroshi Morioka and Aapo Hyvarinen. Connectivity-contrastive learning: Combining causal discovery and representation learning for multimodal data. In International Conference on Artificial Intelligence and Statistics (AISTATS), 2023.
[31] Basil Mustafa, Carlos Riquelme Ruiz, Joan Puigcerver, Rodolphe Jenatton, and Neil Houlsby. Multimodal contrastive learning with limoe: the language-image mixture of experts. In Advances in Neural Information Processing Systems (NeurIPS), 2022.
[32] Ryumei Nakada, Halil Ibrahim Gulluk, Zhun Deng, Wenlong Ji, James Zou, and Linjun Zhang. Understanding multimodal contrastive learning and incorporating unpaired data. In International Conference on Artificial Intelligence and Statistics (AISTATS), 2023.
[33] Natalia Neverova, Christian Wolf, Graham Taylor, and Florian Nebout. Moddrop: adaptive multimodal gesture recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 38(8):1692-1706, 2015.
[34] Manuel T Nonnenmacher, Lukas Oldenburg, Ingo Steinwart, and David Reeb. Utilizing expert features for contrastive learning of time-series representations. In International Conference on Machine Learning (ICML), 2022.
[35] Xiaomin Ouyang, Xian Shuai, Jiayu Zhou, Ivy Wang Shi, Zhiyuan Xie, Guoliang Xing, and Jianwei Huang. Cosmo: Contrastive fusion learning with small data for multimodal human activity recognition. In International Conference on Mobile Computing And Networking (MobiCom), 2022.
[36] Yilmazcan Ozyurt, Stefan Feuerriegel, and Ce Zhang. Contrastive learning for unsupervised domain adaptation of time series. In International Conference on Learning Representations (ICLR), 2023.
[37] Petra Poklukar, Miguel Vasco, Hang Yin, Francisco S Melo, Ana Paiva, and Danica Kragic. Geometric multimodal contrastive representation learning. In International Conference on Machine Learning (ICML), 2022.
[38] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning (ICML), 2021.
[39] Valentin Radu, Nicholas D Lane, Sourav Bhattacharya, Cecilia Mascolo, Mahesh K Marina, and Fahim Kawsar. Towards multimodal deep learning for activity recognition on mobile devices. In ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp), 2016.
[40] Aniruddh Raghu, Payal Chandak, Ridwan Alam, John Guttag, and Collin Stultz. Contrastive pre-training for multimodal medical time series. In NeurIPS Workshop on Learning from Time Series for Health, 2022.
[41] Attila Reiss and Didier Stricker. Introducing a new benchmarked dataset for activity monitoring. In International Symposium on Wearable Computers (ISWC), 2012.
[42] Aaqib Saeed, Tanir Ozcelebi, and Johan Lukkien. Multi-task self-supervised learning for human activity detection. ACM Interact. Mob. Wearable Ubiquitous Technol. (IMWUT), 3(2), 2019.
[43] Mathieu Salzmann, Carl Henrik Ek, Raquel Urtasun, and Trevor Darrell. Factorized orthogonal latent spaces. In International Conference on Artificial Intelligence and Statistics (AISTATS), 2010.
[44] Ruiyuan Song, Dongheng Zhang, Zhi Wu, Cong Yu, Chunyang Xie, Shuai Yang, Yang Hu, and Yan Chen. Rf-urls: unsupervised representation learning for rf sensing. In International Conference on Mobile Computing And Networking (MobiCom), 2022.