The growth of the Internet of Things (IoT) has brought an ever-growing number of connected sensors, continuously streaming large quantities of multimodal data. These data come from a wide range of different sensing modalities and have distinct statistical characteristics over time, which are hardly captured by traditional learning methods. Continual and multimodal learning allows the integration, adaptation, and generalization of knowledge learned from experiential and heterogeneous data to new situations. Therefore, continual and multimodal learning is an important step to enable efficient information inference for IoT systems. CML-IOT welcomes works from diverse communities that introduce algorithmic and systemic approaches to leverage continual learning on multimodal data for applications and real-world computing systems for the Internet of Things.
The Workshop on Continual and Multimodal Learning for Internet of Things (CML-IOT 2022) aims to explore the intersection of continual machine learning and multimodal modeling. The Internet of Things (IoT) has brought an ever-growing amount of multimodal sensing data (e.g., natural language, speech, image, video, audio, virtual reality, WiFi, GPS, RFID, vibration). The statistical properties of this data vary significantly over time and depending on the sensing modality; these differences are hardly captured by conventional learning methods. Continual and multimodal learning allows the integration, adaptation and generalization of knowledge learned from experiential and heterogenous data to new situations. Therefore, continual and multimodal learning is an important step to improve the estimation, utilization, and security of real-world data from IoT systems.
We welcome works addressing these issues in different applications and domains, such as natural language processing, computer vision, human-centric sensing, smart cities, health, etc. We aim to bring together researchers from different areas to establish a multidisciplinary community and share the latest research. We focus on novel learning methods that can be applied on streaming multimodal data with applications to the Internet of Things. Topics of interest include, but are not limited to:
We also welcome continual learning methods that target: data distribution changes caused by the fast-changing dynamic physical environment missing, imbalanced, or noisy data under multimodal data scenarios. Novel applications or interfaces on multimodal data are also related topics.
We welcome works addressing challenges from a wide range of data and sensing modalities, including but not limited to: WiFi, LIDAR, GPS, RFID, visible light communication, vibration, accelerometer, pressure, temperature, humidity, biochemistry, image, video, audio, speech, natural language, AR/VR.
All submissions must use the LaTeX (preferred) or Word styles found here. LaTeX submissions should use the acmart.cls template (sigconf option), with the default 9-pt font. We invite papers of varying length from 2 to 6 pages, plus additional pages for the reference; i.e., the reference page(s) are not counted to the limit of 6 pages. Accepted papers will be included in the ACM Digital Library and supplemental proceedings of the conference. Reviews are not double-blind, and author names and affiliations should be listed.
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