[visionlist] Call for Papers: SI on Multimodal machine learning for human behavior analysis @ ACM TOMM

[Shengping Zhang]

[Apologies for multiple postings]

Special Issue on Multimodal machine learning for human behavior analysis
ACM Transactions on Multimedia Computing, Communications, and Applications
https://tomm.acm.org/CFP_TOMM_SI_human_behavior_analysis.pdf


*** CALL FOR PAPERS ***

Analyzing human behaviors in multimedia data has become one of the most
interesting topics in intelligent multimedia perception. Recently, with the
widespread availability of advanced visual and non-visual sensors and a
growing need of user-friendly interface, integrating multi-modality data
for human behavior analysis, has received a great deal of research
interests from the community of multimedia analysis. Compared with the
traditional single-modality human behavior analysis, multi-modality human
behavior analysis provides deeper level understanding to human
identification and event detection, and a more comprehensive perspective
for understanding the intrinsic interaction and connections of humans.

Although the studies of human behavior analysis in multimodality data are
invaluable for both academia and industry, there are many fundamental
problems unsolved so far, such as learning representation of human
appearance and behaviors from multiple modalities, mapping data from one
modality to another to achieve cross-modality human behavior analysis,
identifying and utilizing relations between elements from two or more
different modalities for comprehensive behavior analysis, fusing
information from two or more modalities to perform a more accurate
prediction, transferring knowledge between modalities and their
representations, and recovering missing modality data given the observed
ones. In the past decade, several multimodal machine learning models have
been developed and shown promising results in some real-world examples such
as multimedia descriptions and retrieval, which prepares us to exploit and
develop effective multimodal machine learning algorithms for addressing
fundamental issues in human behavior analysis.

This special issue aims at providing a forum for researchers from natural
language processing, multimedia, computer vision, speech processing and
machine learning to present recent progress in machine learning research
with applications to multimodal multimedia data. The list of possible
topics includes, but not limited to:

Theories

- Multimodal representation learning

- Multimodal translation and mapping

- Multimodal aligning

- Multimodal fusing and co-learning

Applications

- Multimodal affect recognition including emotion, persuasion and
personality traits

- Multimodal media description including image captioning, video captioning
and visual question answering

- Multimodal action recognition

- Cross-media information retrieval

- Large-scale multimodal datasets

Tutorial or overview papers, creative papers outside the areas listed above
but related to the overall scope of the special issue are also welcome.
Prospective authors can contact the Guest Editors to ascertain interest on
such topics. Submission of a paper to ACM TOMM is permitted only if the
paper has not been submitted, accepted, published, or copyrighted in
another journal. Papers that have been published in conference and workshop
proceedings may be submitted for consideration to ACM TOMM provided that
(i) the authors cite their earlier work;(ii) the papers are not identical;
and (iii) the journal publication includes novel elements (e.g., more
comprehensive experiments). For submission information, please refer to the
ACM TOMM journal guidelines (see https://tomm.acm.org/authors.cfm).
Manuscripts should be submitted through the online system (
https://mc.manuscriptcentral.com/tomm).

*** Important dates ***

Submission deadline: April 15, 2019
First notification: June 15, 2019
Revision submission: August 15, 2019 Notification of acceptance: September
31, 2019 Online publication: March 2020

*** Guest editors ***

Prof. Shengping Zhang,
Harbin Institute of Technology, China, s.zhang at hit.edu.cn

Dr. Huiyu Zhou,
University of Leicester, United Kingdom, hz143 at leicester.ac.uk

Prof. Dong Xu,
University of Sydney, Australia, dong.xu at sydney.edu.au

Prof. M. Emre Celebi,
University of Central Arkansas, USA, ecelebi at uca.edu

Prof. Thierry Bouwmans,
University of La Rochelle, France, thierry.bouwmans at univ-lr.fr

On behalf of the guest editors,
-- 
Dr. Shengping Zhang
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