CBAR 2016, Held in conjunction with IEEE CVPR 2016, in June 2016, Las Vegas, Nevada

1. Workshop Description
 and Objectives
Unconsciously, humans evaluate situations based on environment and social parameters when recognizing emotions in social interactions. Without context, even humans may misunderstand the observed facial, vocal or body behavior. Contextual information, such as the ongoing task (e.g., human-computer vs. human-robot interaction), the identity (male vs. female) and natural expressiveness of the individual (e.g., introvert vs. extrovert), as well as the intra- and inter-personal contexts, help us to better interpret and respond to environment around us. These considerations suggest that attention to context information can deepen our understanding of affect communication (e.g., discrete emotions, affective dimensions such as valence and arousal, different types of moods and sentiment, etc.) for making reliable real-world affect-sensitive applications.

Building upon the success of previous CBAR workshops, this 4th workshop aims to investigate how to efficiently exploit and model context using the cutting edge computer vision and machine learning approaches in order to advance automatic affect recognition. Specifically, the goal is to explore the following questions:
  • Can we leverage the contextual information such as the subject’s age and gender to improve performance of the affect recognition systems? Are the meta-data needed, or can these ‘contextual’ variables automatically and jointly be estimated with the target affect?
  • How can we successfully exploit domain adaptation methods to achieve personalized affect recognition?
  • Can we go beyond the standard domain adaptation to accomplish the context adaptation so as to improve the interpretation and recognition of human affect across different contexts (e.g., not only subjects but also their cultures, tasks, etc., and do so simultaneously)?
  • How can we combine multiple modalities of human affect (e.g., audio, visual, and/or physiological signals) using the contextual information to successfully handle :
-Asynchrony and discordance of different modalities such as face, head/body, voice, and heart rate.
-Innate priority among the modalities.
-Temporal variations in the relative importance of the modalities according to the context.
  • Action recognition in relation to social contexts (e.g., home, work, party, etc.): How can we integrate the influence of social contexts (e.g., individual or dyadic interactions) in automatic recognition of human affect? 
  • Applications: 
-Context-aware clinical applications such as depression severity measurement, pain motoring, and autism screening (e.g. the influence of age, gender, intimate vs. stranger interaction, physician-patient relationship, home vs. hospital environment),  
-Context based and affect-aware intelligent tutors (e.g. learning profile and personality assessments)  

There is a growing research interest of the computer vision and machine learning community in modeling context in various vision-based domains. While significant advances in this direction have been made for object detection and recognition, there has been little progress in leveraging context to improve the computer vision and machine-learning algorithms for automatic affect recognition. This is despite a large body of cognitive evidence emphasizing the importance of context for successful interpretation of human affect. To this end, CVPR provides an ideal environment to gather researchers working on different domains (from low-level image modeling for object detection to high-level modeling of complex spatio-temporal dependencies in human-interaction data) to share their vision on and propose novel approaches for modeling context in affect recognition, such as: modeling human affect in group activities, its temporal reasoning, different levels of hierarchies (from low-level image descriptors to high level interpretation of human affect), as well as context-sensitive fusion of multiple modalities.

2. Topics of Interest
Topics of interest include, but are by no means limited to:
  • Context-sensitive affect recognition from still images or videos
  • Audio and/or physiological data modeling for context-sensitive affect recognition
  • Affect tagging in images, videos or speech using context
  • Modeling human-object interactions for affect recognition
  • Modeling scene context for affect recognition
  • Modeling social contexts for affect recognition
  • Domain adaptation for context-aware affect recognition
  • Deep networks for context-aware affect recognition
  • Multi-modal context-aware fusion for affect recognition
  • Context based corpora recording and annotation
  • Theoretical and empirical analysis of influence of context on affect recognition
  • Context based and affect-aware applications

We also invite both application-driven and theoretical submissions from other related domains focusing on modeling of context for human behavior analysis, including action recognition and human-robot interaction, among others. Works performing evaluation of existing context-sensitive models from other domains (such as object detection and recognition) are also encouraged.

Title: Consensus Bayesian Models for Analysis of Distributed Spatio-Temporal Processes: Human Affect, Crowds, and Beyond.

Abstract: Statistical methods rely on compact summaries of large amounts of data to address problems that may be difficult to tackle with geometric reasoning or physical modeling alone. A basic premise in those settings is that of one central model (however complex it may be) is estimated from a body of data. However, frequently the problems one seeks to address have distributed nature: networks of cameras (placed e.g., on mobile phones) may observe an event distributed in space and time. Moreover, such sensors are frequently carried and controlled by human users. The sensors offer a record of events around the user, affected or unaffected by the user herself, her affective state as well as the social context.  Therefore, we are faced with two critical questions: 1) Is it possible to learn a set of decentralized probabilistic models, each dedicated to one (or a small cluster of) sensors, and yet guarantee that those models will agree in their view of the world, making them effectively equivalent to one centralized model?  2) How do we take into account the affective state of the user in those models and whether and how one can efficiently estimate it from sensory (mostly visual) data.  In this talk I will answer those two questions by reviewing the work in distributed Bayesian learning for large data and human affect modeling in my group. This will be demonstrated on problems such as the distributed 3D structure-from-motion, distributed matrix completion, human emotion and pain intensity modeling, and others.
4. Submission Policy
We call for submission of high-quality papers. The submitted manuscripts should not be submitted to another conference or workshop. Each paper will receive at least two reviews. Acceptance will be based on relevance to the workshop, novelty, and technical quality.
At least one author of each paper must register and attend the workshop to present the paper.
We welcome regular, position, and applications papers. The papers have to be submitted at the following link (EasyChairCBAR2016). 

Accepted papers will be included in the Proceedings of IEEE CVPR 2016 & Workshops.

There will be an Award for the best CBAR paper. 

5. Tentative Deadlines
Submission Deadline:           April 10, 2016
Notification of Acceptance:   TBD
Camera Ready:                     May 1, 2016
Workshop:                             July 1, 2016

6. Organizers 

Zakia Hammal, Ph.D.
The Robotics Institute, Carnegie Mellon University
Pittsburgh, PA, USA

Merlin Teodosia Suarez, Ph.D.
Center for Empathic Human-Computer Interactions
De La Salle University, Manila, Philippines

Ognjen Rudovic, Ph.D.
Intelligent Behaviour Understanding Group
Imperial College London, UK             

7. Program Committee

Busso Carlos, UT-Dallas, USA
Bianchi-Berthouze Nadia, University College London, UK 
Heylen Dirk, University of Twente, The Netherlands

Hess Ursula, Humboldt University, Berlin
Martinez Aleix, The Ohio State University, USA
Mahoor Mohammad, University of Denver, USA
Narayanan Shrikanth, University of Southern California, USA
Pavlovic Vladimir, Rutgers University, USA
Rodrigo  Ma. Mercedes, Ateneo de Manila University
Schuller Bjoern, Imperial College London, UK 
Truong Khiet, University of Twente, The Netherlands
Whitehill Jacob, Harvard University, USA
Yin Lijun, Binghamton University, USA