*Call for Papers*

*Special Issue of IEEE Transactions on Multimedia*

*“Weakly Supervised Learning for Image and Video Understanding”*


With the goal of addressing fine-level image and video understanding tasks
by learning from coarse-level human annotations, WSL is of particular
importance in such a big data era as it can dramatically alleviate the
human labor for annotating each of the structured visual/multimedia data
and thus enables machines to learn from much larger-scaled data but with
the equal annotation cost of the conventional fully supervised learning
methods. More importantly, when dealing with the data from real-world
application scenarios, such as the medical imaging data, remote sensing
data, and audio-visual data, fine-level manual annotations are very limited
and difficult to obtain. Under these circumstances, the WSL-based learning
frameworks, specifically for the WSL-based multi-modality/multi-task
learning frameworks, would bring great benefits. Unfortunately, designing
effective WSL systems is challenging due to the issues of “semantic
unspecificity” and “instance ambiguity”, where the former refers to the
setting where the provided semantic label is at image level rather than
specific instance-level while the latter refers to the ambiguity when
determining an instance sample against the instance part or instance
cluster. Principled solutions to address these problems are still
under-studied. Nowadays, with the rapid development of advanced machine
learning techniques, such as the Graph Convolutional Networks, Capsule
Networks, Transformers, Generative Adversarial Networks, and Deep
Reinforcement Learning models, new opportunities have emerged for solving
the problems in WSL and applying WSL to richer vision and multimedia
tasks. This
special issue aims at promoting cutting-edge research along this direction
and offers a timely collection of works to benefit researchers and
practitioners. We welcome high-quality original submissions addressing both
novel theoretical and practical aspects related to WSL, as well as the
real-world applications based on WSL approaches.


Topics of interests include, but are not limited to:

-          Multi-modality weakly supervised learning theory and framework;

-          Multi-task weakly supervised learning theory and framework;

-          Robust learning theory and framework;

-          Audio-visual learning under weak supervision;

-          Weakly supervised spatial/temporal feature learning;

-          Self-supervised learning frameworks and applications;

-  Graph Convolutional Networks/Graph Neural Networks-based weakly
supervised learning frameworks;

-          Deep Reinforcement Learning for weakly supervised learning;

-          Emerging vision and multimedia tasks with limited supervision;


Manuscript submission:           15th January 2021

Preliminary results:                  15th April 2021

Revisions due:                          1st June 2021

Notification:                             15th July 2021

Final manuscripts due:             15th August 2021

Anticipated publication:           4th quarter of 2021


Papers should be formatted according to the IEEE Transactions on Multimedia
guidelines for authors (see:
http://www.signalprocessingsociety.org/tmm/tmm-author-info/). By
submitting/resubmitting your manuscript to these Transactions, you are
acknowledging that you accept the rules established for publication of
manuscripts, including agreement to pay all over-length page charges, color
charges, and any other charges and fees associated with publication of the
manuscript. Manuscripts (both 1-column and 2-column versions are required)
should be submitted electronically through the online IEEE manuscript
submission system at http://mc.manuscriptcentral.com/tmm-ieee. When
selecting a manuscript type, the authors must choose WSL Special Issue. All
submitted papers will go through the same review process as the regular TMM
paper submissions. Referees will consider originality, significance,
technical soundness, clarity of exposition, and relevance to the special
issue topics above.


Dingwen Zhang, Xidian University, China

Chuang Gan, MIT and MIT-IBM Watson AI Lab, USA

Enrico Magli, Politecnico di Torino, Italy

David Crandall, Indiana University, USA

Junwei Han, Northwestern Polytechnical University, China

Fatih Porikli, Australian National University, Australia

Dingwen Zhang
Xidian University
Carnegie Mellon University