Continual and Multimodal Learning for Internet of Things

September 13, 2020 • Cancun, Mexico

A UbiComp 2020 Workshop


Internet of Things (IoT) provides streaming, large-amount, and multimodal sensing data over time. The statistical properties of these data are often significantly different by sensing modalities and temporal traits, which are hardly captured by conventional learning methods. Continual and multimodal learning allows integration, adaptation and generalization of the knowledge learnt from previous experiential data collected with heterogeneity 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 devices.

Call for Papers

This workshop aims to explore the intersection and combination of continual machine learning and multimodal modeling with applications in Internet of Things. The workshop welcomes works addressing these issues in different applications and domains, such as human-centric sensing, smart cities, health and wellness, privacy and security, etc. We aim at bringing together researchers from different areas to establish a multidisciplinary community and share the latest research.

We focus on the novel learning methods that can be applied on streaming multimodal data:

  • continual learning
  • transfer learning
  • federated learning
  • few-shot learning
  • multi-task learning
  • reinforcement learning
  • learning without forgetting
  • individual and/or institutional privacy
  • balance on-device and off-device learning
  • manage high volume data flow

  • We also welcome continual learning methods that target:

  • data distribution changed caused by the fast-changing dynamic physical environment
  • missing, imbalanced, or noisy data under multimodal sensing scenarios

  • Novel applications or interfaces on streaming multimodal data are also related topics.

    As examples, the data modalities include but not limited to: WiFi, GPS, RFID, vibration, accelerometer, pressure, temperature, humidity, biochemistry, image, video, audio, speech, natural language, virtual reality, etc.

    Important Dates

  • Submission deadline: July 10, 2020
  • Notification of acceptance: July 24, 2020
  • Deadline for camera ready version: July 31, 2020
  • Workshop: September 13, 2020
  • Submit Now

    Submission Guidelines

    Please submit papers using the ACM SIGCHI portrait template. 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.


    Workshop Chairs (Feel free to contact us by, if you have any questions.)
  • Susu Xu (Qualcomm AI Research)
  • Tong Yu (Samsung Research America)
  • Shijia Pan (UC Merced)

  • Advising Committee
  • Pei Zhang (Carnegie Mellon University)
  • Hae Young Noh (Stanford University)
  • Joseph Soriaga (Qualcomm AI Research)
  • Steve Gu (AiFi Inc.)

  • Technical Program Committee
  • Xiaoxi Zhang (Carnegie Mellon University)
  • Sheng Li (University of Georgia)
  • Xidong Pi (Uber ATG)
  • Zhihan Fang (Rutgers University)
  • Wei Ma (The Hong Kong Polytechnic University)
  • Adeola Bannis (Carnegie Mellon University)
  • Hossein Hosseini (Qualcomm AI Research)
  • Mostafa Mirshekari (Stanford University)
  • Rui Ma (Tsinghua University)
  • Shanghang Zhang (UC Berkeley)
  • Aniruddha Basak (Amazon)
  • Handong Zhao (Adobe Research)

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