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Tue, 3 Nov 2020 11:19:51 +0100
Vitomir Struc <[log in to unmask]>
"ACM SIGCHI General Interest Announcements (Mailing List)" <[log in to unmask]>
Vitomir Struc <[log in to unmask]>
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11th International Workshop on Human Behavior Understanding (HBU)
Focus theme: Multi-source aspects of behavioral understanding

Held in conjunction with WACV 2021

Paper submission deadline (extended to): November 5th, 2020
Notifications: November 24th, 2020

Abhijit Das, Indian Statistical Institute, Kolkata, India
Qiang Ji, Rensselaer Polytechnic Institute, United States
Umapada Pal, Indian Statistical Institute, Kolkata, India
Albert Ali Salah, Utrecht University, The Netherlands
Vitomir Štruc, University of Ljubljana, Slovenia

Marija Ivanovska, University of Ljubljana, Slovenia

Domains for human behaviour understanding predominantly (e.g., 
multimedia, human-computer interaction, robotics, affective computing 
and social signal processing) rely on advanced pattern recognition 
techniques to automatically interpret complex behavioural patterns 
generated when humans interact with machines or with other agents. This 
is a challenging research area where many issues are still open, 
including the joint modelling of behavioural cues taking place at 
different time scales, the inherent uncertainty of machine detectable 
evidence of human behaviour, the mutual influence of people involved in 
interactions, the presence of long term dependencies in observations 
extracted from human behaviour, and the important role of dynamics in 
human behaviour understanding. Computer vision is a key technology for 
analysis and synthesis of human behaviour but stands to gain much from 
multi-modality and multi-source processing, in terms of improving 
accuracy, resource use, robustness, and contextualization.

This workshop, organized as part of WACV 2021, will gather researchers 
dealing with the problem of modelling human behaviour under its multiple 
facets (expression of emotions, display of relational attitudes, the 
performance of an individual or joint actions, etc.), with particular 
attention to multi-source aspects, including multi-sensor, 
multi-participant and multi-modal settings. Example challenges are the 
additional resource and robustness constraints, explorations in 
information fusion, social and contextual aspects of interactions, and 
building multi-source representations of social and affective signals 
with the goal of advancing the state-of-the-art.

The HBU workshops, previously organized as satellite events to major 
conferences in different disciplines such as ICPR’10, AMI’11, IROS’12, 
ACMMM’13, ECCV’14, UBICOMP’15, ACMMM’16, FG’18, ECCV’18, ICCV’19 have a 
unique aspect of fostering cross-pollination of disciplines, bringing 
together researchers from a variety of fields, such as computer vision, 
HCI, artificial intelligence, pattern recognition, interaction design, 
ambient intelligence, psychology and robotics. The diversity of human 
behaviour, the richness of multimodal data that arises from its 
analysis, and the multitude of applications that demand rapid progress 
in this area ensure that the HBU Workshops provide a timely and relevant 
discussion and dissemination platform. For HBU@WACV, we particularly 
solicit contributions on human behaviour understanding that combine 
multiple sources of information, be it across modalities, sensors, or 
subjects under observation. The workshop solicits papers on general 
topics related to human behaviour understanding, but with a distinct 
focus on multi-source solutions.

Topics of interest include, but are not limited to:

     + Multimodal solutions for human behaviour modelling and analysis
     + Multimodal solutions towards behavioural biometrics (gait, 
handwriting, keystroke dynamics, etc.)
     + Methods for multi-instance learning in behavioural understanding,
     + Analysis of multi-participant settings and of social interactions,
     + Multi-instance representation for characterizing human health, 
     + Deep learning for multi-party interactions
     + Multimodal deep learning for behaviour understanding
     + Adversarial learning approaches
     + Related sensor technologies
     + Information fusions approach for behaviour analysis
     + Realistic behaviour synthesis in multiple modalities and for 
multi-party settings
     + Mobile and wearable systems for behaviour monitoring
     + Datasets and benchmarks
     + Related applications

Sandipan Banerjee, Affectiva
Ross Beveridge, Colorado State University
Francois Bremond, INRIA
Carlos Busso, University of Texas at Dallas
Antitza Dantcheva, INRIA
Hamdi Dibeklioğlu, Bilkent University
Hugo Jair Escalante, National Institute of Astrophysics, Optics and 
Electronics (INAOE)
Sergio Escalera, CVC and University of Barcelona
Nicholas Evans, EURECOM
Jordi Gonzalez, UA Barcelona
Laszlo Jeni, Carnegie Mellon University
Heysem Kaya, Utrecht University
Aythami Morales Moreno, UAM
Atsushi Nakazawa, Kyoto University
Sebastian Nowozin, Microsoft Corporation
Catharine Oertel, TU Delft
Itır Önal Ertuğrul, Tilburg University
Catherine Pelachaud, French National Centre for Scientific Research (CNRS)
Ronald Poppe, Utrecht University
Elisa Ricci, University of Trento
Zhenan Sun, CASIA
Giovanna Varni, Telecom ParisTech
Roberto Vezzani, University of Modena and Reggio Emilia
Gualtiero Volpe, University of Genova

Submission instruction can be found at

Rachael Jack, University of Glasgow
Louis-Philippe Morency, Carnegie Mellon University

Please feel free to reach out for further details.

Abhijit Das, Qiang Ji, Umapada Pal, Albert Ali Salah, Vitomir Štruc
HBU 2021 Organizers

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