COCOA: Cross Modality Contrastive Learning for Sensor Data

• Published in: arXiv.org 2022-08
• Main Authors: Deldari, Shohreh, Xue, Hao, Aaqib Saeed, Smith, Daniel V, Salim, Flora D
• Format: Article
• Language: English
• Subjects: Cocoa; Computer vision; Cross correlation; Natural language processing; Representations; Sensors; Supervised learning; Ubiquitous computing
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.2208.00467

Self-Supervised Learning (SSL) is a new paradigm for learning discriminative representations without labelled data and has reached comparable or even state-of-the-art results in comparison to supervised counterparts. 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 labelled data.