[visionlist] AI4Media: ACM TOMM Special Issue on Realistic Synthetic Data: Generation, Learning, Evaluation
ioannakoroni at csd.auth.gr
Tue Jan 24 04:12:52 -04 2023
The ACM Transactions on Multimedia Computing, Communications, and Applications organises a Special Issue on "Realistic Synthetic Data: Generation, Learning, Evaluation". The Special Issue is endorsed by the AI4Media project and the Guest Editors are members of the AI4Media consortium.
The call for papers can be found below and in the attachment. The submission deadline is March 31st, 2023.
The Topics of Interest include:
* Synthetic data for various modalities, e.g., signals, images, volumes, audio, etc.
* Controllable generation for learning from synthetic data.
* Transfer learning and generalization of models.
* Causality in data generation.
* Addressing bias, limitations, and trustworthiness in data generation.
* Evaluation measures/protocols and benchmarks to assess quality of synthetic content.
* Open synthetic datasets and software tools.
* Ethical aspects of synthetic data.
Please consider submitting your work to this special issue!
Call-for-Papers: ACM TOMM SI on Realistic Synthetic Data: Generation, Learning, Evaluation
[Apologies for multiple postings]
ACM Transactions on Multimedia Computing, Communications, and Applications
Special Issue on Realistic Synthetic Data: Generation, Learning, Evaluation
Impact Factor 4.094
Submission deadline: 31 March 2023
*** CALL FOR PAPERS ***
Bogdan Ionescu, Universitatea Politehnica din Bucuresti, România
Ioannis Patras, Queen Mary University of London, UK
Henning Muller, University of Applied Sciences Western Switzerland, Switzerland
Alberto Del Bimbo, Università degli Studi di Firenze, Italy
In the current context of Machine Learning (ML) and Deep Learning
(DL), data and especially high-quality data are central for ensuring
proper training of the networks. It is well known that DL models
require an important quantity of annotated data to be able to reach
their full potential. Annotating content for models is traditionally
made by human experts or at least by typical users, e.g., via
crowdsourcing. This is a tedious task that is time consuming and
expensive -- massive resources are required, content has to be curated
and so on. Moreover, there are specific domains where data
confidentiality makes this process even more challenging, e.g., in the
medical domain where patient data cannot be made publicly available,
With the advancement of neural generative models such as Generative
Adversarial Networks (GAN), or, recently diffusion models, a promising
way of solving or alleviating such problems that are associated with
the need for domain specific annotated data is to go toward realistic
synthetic data generation. These data are generated by learning
specific characteristics of different classes of target data. The
advantage is that these networks would allow for infinite variations
within those classes while producing realistic outcomes, typically
hard to distinguish from the real data. These data have no proprietary
or confidentiality restrictions and seem a viable solution to generate
new datasets or augment existing ones. Existing results show very
promising results for signal generation, images etc.
Nevertheless, there are some limitations that need to be overcome so
as to advance the field. For instance, how can one control/manipulate
the latent codes of GANs, or the diffusion process, so as to produce
in the output the desired classes and the desired variations like real
data? In many cases, results are not of high quality and selection
should be made by the user, which is like manual annotation. Bias may
intervene in the generation process due to the bias in the input
dataset. Are the networks trustworthy? Is the generated content
violating data privacy? In some cases one can predict based on a
generated image the actual data source used for training the network.
Would it be possible to train the networks to produce new classes and
learn causality of the data? How do we objectively assess the quality
of the generated data? These are just a few open research questions.
In this context, the special issue is seeking innovative algorithms
and approaches addressing the following topics (but is not limited
- Synthetic data for various modalities, e.g., signals, images,
volumes, audio, etc.
- Controllable generation for learning from synthetic data.
- Transfer learning and generalization of models.
- Causality in data generation.
- Addressing bias, limitations, and trustworthiness in data generation.
- Evaluation measures/protocols and benchmarks to assess quality of
- Open synthetic datasets and software tools.
- Ethical aspects of synthetic data.
- Submission deadline: 31 March 2023
- First-round review decisions: 30 June 2023
- Deadline for revised submissions: 31 July 2023
- Notification of final decisions: 30 September 2023
- Tentative publication: December 2023
Prospective authors are invited to submit their manuscripts
electronically through the ACM TOMM online submission system (see
https://mc.manuscriptcentral.com/tomm) while adhering strictly to the
journal guidelines (see https://tomm.acm.org/authors.cfm). For the
article type, please select the Special Issue denoted SI: Realistic
Synthetic Data: Generation, Learning, Evaluation.
Submitted manuscripts should not have been published previously, nor
be under consideration for publication elsewhere. If the submission is
an extended work of a previously published conference paper, please
include the original work and a cover letter describing the new
content and results that were added. According to ACM TOMM publication
policy, previously published conference papers can be eligible for
publication provided that at least 40% new material is included in the
For questions and further information, please contact Bogdan Ionescu /
bogdan.ionescu at upb.ro <mailto:bogdan.ionescu at upb.ro> .
The Special Issue is endorsed by the AI4Media "A Centre of Excellence
delivering next generation AI Research and Training at the service of
Media, Society and Democracy" H2020 ICT-48-2020 project
On behalf of the Guest Editors,
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