Special Issue on "Looking At People: Analyzing Human Behavior from Social Media Data" International Journal of Computer Vision https://www.springer.com/computer?SGWID=0-146-6-1390546-0
*** CALL FOR PAPERS *** Although great advances have been obtained in the "Looking at People" field, it is only recently that attention has focused on problems connected to more complex and subconscious behavior. For instance, personality and social behavior are only starting to be explored from the computer vision and multimedia information processing perspectives. This is often due to a lack of data and benchmarks to evaluate these type of tasks.
Nevertheless, the availability of massive amounts of multimodal information together with the dominance of social networks as a fundamental channel where users interact, have attracted the interest of the community in this direction of research. Tools for effectively analyzing these sort of behaviors have a major impact into everyone's life, with applications in health (e.g., support for mental disorders), security (e.g., forensics, preventive applications), human computer/machine/robot interaction (e.g., affective/interactive interfaces) and even entertainment (e.g., user-tailored systems).
This special issue focuses in all aspects of computer vision and pattern recognition devoted to the automatic analysis of human behavior in social media from visual and multimodal information. The focus is on the analysis of human behavior that is not visually obvious, i.e., unconscious behavior and situations in which the sole visual analysis is insufficient to provide a satisfactory solution. Submissions in other aspects of looking at people may be considered as well.
Prospective articles should make fundamental or practical contributions to the field. Topics of interest include (but are not limited to): - Human behavior analysis from visual and multimodal information, with emphasis on unconscious behaviors, including, but not limited to: personality analysis, deception detection, social behavior analysis - All aspects of human behavior analysis in the context of social networks using multimodal information, including, but not limited to: gesture/action, emotion recognition, personality analysis and human-computer interaction - Personality analysis and deception detection from multimodal information, including textual, visual, and audible information - Information retrieval, categorization and clustering of social networks data, including images, text, and videos for the analysis of human behavior - Analysis of human intention from social networks data involving multimodal information - New tasks, data sets and benchmarks on human behavior analysis from multimodal information - Multimodal machine learning, deep learning, active learning, and transfer learning for human behavior analysis in social media - Multimodal zero-shot learning, and unsupervised learning for the analysis of unconscious human behaviors - Crowdsourcing, community contributions, and social multimedia - Information fusion for the analysis of human behavior in the context of social networks - Large-scale and web-scale multimodal analysis of social media - Explainability and fairness in multimodal AI systems for human behavior analysis - Applications of unconscious behavior analysis methods, e.g., medicine, sports, commerce, lifelogs, travel, security, environment.
*** Submission guidelines *** All the papers should be full journal length submissions and follow the guidelines set out by International Journal of Computer Vision: https://link.springer.com/journal/11263
Manuscripts should be submitted online at: https://www.editorialmanager.com/visi/ choosing "S.I. : Analyzing Human Behavior from Social Media Data" as article type.
When uploading your paper, please ensure that your manuscript is marked as being for this special issue. Information about the manuscript (title, full list of authors, corresponding author's contact, abstract, and keywords) should be also sent to the corresponding editors (see information below).
Submitted papers should present original, unpublished work, relevant to at least one of the topics of the special issue. All submitted papers will be evaluated on the basis of relevance, significance of contribution, technical quality, scholarship, and quality of presentation, by at least three independent reviewers. It is the policy of the journal that no submission, or substantially overlapping submission, be published or be under review at another journal or conference at any time during the review process.
*** Important dates *** Manuscripts Due: 15 March 2019 Publication: 1st quarter of 2020
*** Guest editors *** Hugo Jair Escalante (hugo.jair at gmail.com), INAOE, Mexico & ChaLearn, USA Bogdan Ionescu, University Politehnica of Bucharest, Romania Esaú Villatoro, UAM-C, Mexico Gabriela Ramírez, UAM-C, Mexico Sergio Escalera, Computer Vision Center (UAB) & University of Barcelona, Spain Martha Larson, Radboud University & Delft University of Technology, Netherlands Henning Müller, University of Applied Sciences Western Switzerland (HES-SO), Switzerland Isabelle Guyon, ChaLearn, Berkeley, California, USA
On behalf of the guest editors,
Prof. Bogdan IONESCU ETTI - University Politehnica of Bucharest http://campus.pub.ro/lab7/bionescu/