posted by organizer: hesamsagha || 1313 views || tracked by 6 users: [display]

Big-Affect 2017 : Utilising Big Unlabelled and Unmatched Data for Affective Computing

FacebookTwitterLinkedInGoogle

Link: https://sites.google.com/view/acii17ubuudac/accueil
 
When Oct 23, 2017 - Oct 26, 2017
Where San Antonio, Texas
Submission Deadline May 2, 2017
Notification Due Jul 14, 2017
Final Version Due Aug 18, 2017
Categories    affective computing   big data   transfer learning   zero-shot learning
 

Call For Papers

There has been lots of research toward affect recognition through different modalities such as speech, video, and text. Despite these great efforts, the performed analyses are often limited to small collected datasets which consequently makes generated models barely generalisable to other recording scenarios. This lack of `big' labelled data for affective computing hampers creating deep models, which have proved their substantial effectiveness, so far, mostly in related fields such as speech and video recognition. Thanks to the popularity of social multimedia, collecting audiovisual and textual data has become a somewhat easy task. Nonetheless, labeling such data demands a huge amount of (expert) human work, which can be expensive and time-consuming. Additionally, collected data may not have high quality and therefore, may not be sufficiently reliable to be used for training a model. Furthermore, collected data from different sources may be highly dissimilar, which can also lead to performance degradation. Therefore, in this special session, we seek approaches that aim to increase the number of reliable labelled data with less human effort as well as to match data distributions between labelled and un- or partially-labelled corpora. This will be a crucial step to lead Affective Computing to industrial level and bring related everyday applications into real life.

Topics (indication, not limited to):

* semi-supervised learning and active learning
* zero resource technologies, as unsupervised learning
* transfer learning for domain/model adaptation
* using weak labels and co-training
* crowdsourcing for collecting and annotation large-scale data
* affective data augmentation and synthesis
* reinforcement learning
* cloud/distributed computing algorithms for big affective data
* applications (such as cross-language cross-cultural adaptation, cross-modality transfer learning, ...)

For further details see https://sites.google.com/view/acii17ubuudac/accueil​

Related Resources

ICDM 2019   19th Industrial Conference on Data Mining ICDM 2019
KomIS@ACM-SAC 2019   ACM SAC 2019 - KomIS track: Application of AI and Big Data Analytics
ISBDAI 2018   2018 International Symposium on Big Data and Artificial Intelligence
DSA 2019   The Frontiers in Intelligent Data and Signal Analysis DSA 2019
PerFoT 2019   2019 International Workshop on Pervasive Flow of Things (Co-located with IEEE PerCom 2019)
COMPSAC 2019   COMPSAC 2019: Data Driven Intelligence for a Smarter World
MDM 2019   20th IEEE International Conference on Mobile Data Management
AI 2019   Artificial Intelligence
MLDM 2019   15th International Conference on Machine Learning and Data Mining MLDM 2019
SCAI 2018   7th International Conference On Soft Computing, Artificial Intelligence And Applications