The Fourth Workshop on Continual and Multimodal Learning for Internet of Things

November 06, 2022 • Boston, Massachusetts, USA

Co-Located with SenSys 2022

Previous Editions: CML-IOT'21, CML-IOT'20, CML-IOT'19

About CML-IOT

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.



Call for Papers

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:

  • Continual learning
  • Transfer learning
  • Federated learning
  • Few-shot learning
  • Multi-task learning
  • Reinforcement learning
  • Learning without forgetting
  • Individual and/or institutional privacy
  • Methods and architectures for partitioning on-device and off-device learning
  • Managing high volumes of data flow

  • 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.

    Important Dates

  • Submission deadline: September 05, 2022, AoE September 19, 2022, AoE
  • Notification of acceptance: October 03, 2022, AoE October 10, 2022, AoE
  • Deadline for camera ready version: October 17, 2022, AoE
  • Workshop: November 06, 2022
  • Submit Now



    Submission Guidelines

    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.



    Keynote

    Coming Soon!



    Organizers

    Workshop Chairs (Feel free to contact us by cmliot2022@gmail.com, if you have any questions.)
  • Stephen Xia (Columbia University)
  • Jingxiao Liu (Stanford University)
  • Tong Yu (Adobe Research)
  • Handong Zhao (Adobe Research)
  • Ruiyi Zhang (Adobe Research)


  • Steering Committee
  • Nicholas Lane (University of Cambridge and Samsung AI)
  • Lina Yao (University of New South Wales)
  • Jennifer Healey (Adobe Research)
  • Xiaofan (Fred) Jiang (Columbia University)
  • Hae Young Noh (Stanford University)
  • Shijia Pan (University of California Merced)
  • Susu Xu (Stony Brook University)


  • Technical Program Committee
  • Winston Chen (MIT)
  • Karthik Dantu (University at Buffalo)
  • Mi Zhang (Ohio State University)
  • Jingping Nie (Columbia University)
  • Jorge Ortiz (Rutgers University)
  • Yang Gao (Northwestern University)
  • Shibo Zhang (HP Labs)
  • Wei Ma (The Hong Kong Polytechnic University)
  • Yujie Wei (Meta)
  • Bingqing Chen (Bosch Center for AI)
  • Shuo Li (Flexport)
  • Chulhong Min (Nokia Bell Labs)
  • Zhanpeng Jin (University at Buffalo)
  • VP Nguyen (University of Texas at Arlington)
  • Anh Nguyen (University of Montana)


  • Program

    TBD

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