Conferences: CGI 2022 Call for Papers

March 3rd, 2022 Daniela Lopez de Luise

CALL FOR PAPERS CGI2022

COMPUTER GRAPHICS INTERNATIONAL, CGI2022, ONLINE , 12-16 September, 2022

Computer Graphics International, CGI2022 will be ONLINE from 12-16 September, 2022. CGI is one of the oldest annual international conferences on Computer Graphics in the world. Researchers are invited to share their experiences and novel achievements in various fields of Computer Graphics. Former recent CGI conferences have been held in Sydney, Australie (2014), Strasbourg, France, (2015), Heraklion, Greece (2016), Yokohama, Japan (2017) , Bintan, Indonesia (2018), and Calgary in  Canada (2019). CGI has become virtual since 2020 due to the pandemic.

This year, CGI2022 is organized by MIRALab, University of Geneva, Switzerland and supported by the Computer Graphics Society (CGS). The Visual Computer is the official journal of the Computer Graphics Society.

The main topics of the CGI 2022 conference are the following:

  • Rendering Techniques
  • Geometric Computing
  • Metaverse (VR/MR/XR)
  • Shape and Surface Modeling
  • Physically Based Modeling
  • Computer Vision for Computer Graphics
  • Scientific Visualization
  • Data Compression for Graphics
  • Medical Imaging
  • Computational Geometry
  • Image Based Rendering
  • Computational Photography
  • Computer Animation
  • Visual Analytics
  • Shape Analysis and Image Retrieval
  • Volume Rendering
  • Digital Cultural Heritage
  • Computational Fabrication
  • Image Processing & Analysis
  • 3D Reconstruction
  • Global Illumination
  • Graphical Human-Computer Interaction
  • Digital Humans
  • Saliency Methods
  • Shape Matching
  • Sketch-based Modelling
  • Robotics and Vision
  • Stylized Rendering
  • Textures
  • Machine Learning for Graphics

CGI2022 papers can be submitted either on March 10 for possible publication in the journal Visual Computer or June 5 for possible publication in a Proceedings book. This year, in addition, we organize two special sessions, one on Metaverse (VR, MR, XR) and the other one in Digital Cultural Heritage. The accepted papers for the special session on Metaverse will be published in the VRIH journal (Virtual Reality and Intelligent Hardware journal published by Science Press). The accepted papers for the special session on Digital Innovation in Cultural Heritage will be published by CAVW journal (Computer Animation and Virtual Worlds)  published by Wiley.

GENERAL GUIDELINES FOR PAPERS SUBMISSIONS

The scientific program of the conference will include accepted papers from the first call for papers and these accepted papers will be published by Springer in the Visual Computer Journal (impact factor 2.52) by Springer-Verlag.

The accepted papers from the second call for papers will be included in the CGI conference Proceedings published by LNCS, Springer.

The accepted papers for the special session Metaverse will be published in the VRIH journal (Virtual Reality and Intelligent Hardware) journal published by Science Press.

The accepted papers for the special session in Digital Innovation in Cultural Heritage will be published in the CAVW journal (Computer Animation and Virtual Worlds)  published by Wiley.

Note that for ALL submissions, the review process is double blind, which requires the paper and all supplemental materials to be anonymous. Ensure that self-referencing is anonymous (refer to your full name rather than “I” or “we”). Avoid providing information that may identify the authors in the acknowledgements (e.g. co-workers and grant IDs) and in the supplemental material (e.g. titles in the movies, or attached papers). Avoid providing links to websites that identify the authors. Violation of any of these guidelines will lead to rejection without review.

 

IMPORTANT DATES

Conference, Special Sessions and Workshops September 12-16, 2022

 

Web site:  http://www.cgs-network.org/cgi22/

Contact: cgi2022@miralab.ch

 

Visual Computer papers submission

Submission deadline: March 10, 2022, midnight CET

Preliminary notification: April 15, 2022

Deadline to Receive Revised Papers From Authors: May 20, 2022

Final Notification of Revised Papers: July 5, 2022

 

CGI proceedings, VRIH, and CAVW submission

Submission deadline: June 5, 2022

Paper notification July 5, 2022

Camera-ready July 24, 2022

General Chair

Nadia Magnenat Thalmann, University of Geneva, Switzerland

Program Chairs


Jinman Kim, University of Sydney, Australia

George Papagiannakis, University of Crete, Greece

Bin Sheng, Shanghai Jiao Tong University, China

Daniel Thalmann, EPFL, Switzerland

Publication LNCS Chair

Marina Gavrilova, University of Calgary, Canada

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CfP: Special Issue on Imbalanced Learning (Machine Learning Journal) – Deadline 4/4

March 2nd, 2022 Daniela Lopez de Luise
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————————
Researcher @ INESC TEC
Invited Professor @ Faculty of Sciences, University of Porto

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Special issue on “Biometrics at a distance in the Deep Learning era” – IEEE J-STSP

March 2nd, 2022 Daniela Lopez de Luise
Deep Learning era”
IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING (J-STSP)
https://signalprocessingsociety.org/blog/ieee-jstsp-special-issue-biometrics-distance-deep-learning-era

Biometrics at a distance (e.g., gait recognition, person 
re-identification, etc.) is a particular case of biometric analysis 
that usually does not require the conscious participation of the 
target subject, being non-invasive at the same time. However, the 
sample acquisition is almost always affected by adverse conditions, 
e.g., the lack of details due to the distance itself, so that the 
robustness to distortions of adopted biometric methods is of paramount 
importance. This is a well-established topic in the field of 
information forensics and security. With the arrival of the Deep 
Learning era, new approaches have started to emerge in dealing with 
this task. However, in contrast to other computer vision and machine 
learning problems, as general image/video classification, one of the 
main challenges that has to be addressed in this type of biometric 
problem, amongst others, is the lack or limited amount of available 
annotated data sets for effectively training deep models.
The aim of this special issue is to gather and promote novel 
deep-learning based approaches for addressing the task of biometrics 
at a distance. Specifically, we are interested in works that propose 
new methods to improve the recognition accuracy, the computational 
burden and/or the scalability of the domain of application for 
biometrics, being the application of the deep learning paradigm the 
main component. Special attention will be paid to privacy protection 
and data security in the context of biometrics. In addition, new large 
realistic annotated datasets for the related tasks are welcome.

Topics
================
The topics of interest for this special issue include, but are not 
limited to, the following ones:
*         Gait recognition with Deep Learning
*         Face recognition (low resolution) at a distance with Deep Learning
*         Person re-identification with Deep Learning
*         Soft biometrics at a distance with Deep Learning
*         Multimodal biometrics at a distance with Deep Learning
*         Heterogeneous and cross-modal biometrics at a distance with 
Deep Learning
*         Information fusion for biometrics with Deep Learning
*         Incremental learning for biometrics at a distance with Deep Learning
*         Semi- and weakly-supervised learning for biometrics at a 
distance with Deep Learning
*         Algorithms for effective transfer learning applied to 
biometrics at at distance
*         Multi-task learning applied to biometrics at at distance
*         Privacy protection and data security applied to Biometrics 
at a distance
*         Processing and enhancement of low-quality biometric data

Important Dates
================
* Submissions due    31/July/2022
* First Review due    30/September/2022
* Revised manuscript due    30/November/2022
* Second review due    15/January/2023
* Final manuscript due:    28/February/2023

Guest Editors
================
Manuel J. Marin-Jimenez (Lead GE), University of Cordoba, Spain. 
Email: mjmarinATuco.es
Shiqi Yu, SUSTech, China. Email: yusqATsustech.edu.cn
Yasushi Makihara, Osaka University, Japan. Email: 
makiharaATam.sanken.osaka-u.ac.jp
Vishal Patel, Johns Hopkins University, USA. Email: vpatel36ATjhu.edu
Maria de Marsico, Sapienza Università di Roma, Italy. Email: 
demarsicoATdi.uniroma1.it
Maneet Singh, AI Garage-Mastercard, India. Email: maneetsATiiitd.ac.in

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Special issue on “Biometrics at a distance in the Deep Learning era” – IEEE J-STSP

March 2nd, 2022 Daniela Lopez de Luise
Call for papers: Special issue on “Biometrics at a distance in the Deep Learning era”
IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING (J-STSP)
https://signalprocessingsociety.org/blog/ieee-jstsp-special-issue-biometrics-distance-deep-learning-era

Biometrics at a distance (e.g., gait recognition, person re-identification, etc.) is a particular case of biometric analysis that usually does not require the conscious participation of the target subject, being non-invasive at the same time. However, the sample acquisition is almost always affected by adverse conditions, e.g., the lack of details due to the distance itself, so that the robustness to distortions of adopted biometric methods is of paramount importance. This is a well-established topic in the field of information forensics and security. With the arrival of the Deep Learning era, new approaches have started to emerge in dealing with this task. However, in contrast to other computer vision and machine learning problems, as general image/video classification, one of the main challenges that has to be addressed in this type of biometric problem, amongst others, is the lack or limited amount of available annotated data sets for effectively training deep models.
The aim of this special issue is to gather and promote novel deep-learning based approaches for addressing the task of biometrics at a distance. Specifically, we are interested in works that propose new methods to improve the recognition accuracy, the computational burden and/or the scalability of the domain of application for biometrics, being the application of the deep learning paradigm the main component. Special attention will be paid to privacy protection and data security in the context of biometrics. In addition, new large realistic annotated datasets for the related tasks are welcome.

Topics
================
The topics of interest for this special issue include, but are not limited to, the following ones:
*         Gait recognition with Deep Learning
*         Face recognition (low resolution) at a distance with Deep Learning
*         Person re-identification with Deep Learning
*         Soft biometrics at a distance with Deep Learning
*         Multimodal biometrics at a distance with Deep Learning
*         Heterogeneous and cross-modal biometrics at a distance with Deep Learning
*         Information fusion for biometrics with Deep Learning
*         Incremental learning for biometrics at a distance with Deep Learning
*         Semi- and weakly-supervised learning for biometrics at a distance with Deep Learning
*         Algorithms for effective transfer learning applied to biometrics at at distance
*         Multi-task learning applied to biometrics at at distance
*         Privacy protection and data security applied to Biometrics at a distance
*         Processing and enhancement of low-quality biometric data

Important Dates
================
* Submissions due    31/July/2022
* First Review due    30/September/2022
* Revised manuscript due    30/November/2022
* Second review due    15/January/2023
* Final manuscript due:    28/February/2023

Guest Editors
================
Manuel J. Marin-Jimenez (Lead GE), University of Cordoba, Spain. Email: mjmarinATuco.es
Shiqi Yu, SUSTech, China. Email: yusqATsustech.edu.cn
Yasushi Makihara, Osaka University, Japan. Email: makiharaATam.sanken.osaka-u.ac.jp
Vishal Patel, Johns Hopkins University, USA. Email: vpatel36ATjhu.edu
Maria de Marsico, Sapienza Università di Roma, Italy. Email: demarsicoATdi.uniroma1.it
Maneet Singh, AI Garage-Mastercard, India. Email: maneetsATiiitd.ac.in

Read More »

CRNS Talk Series (3) – Live Talk by Dr. Séverin Lemaignan – PAL Robotics, Spain

March 2nd, 2022 Daniela Lopez de Luise
The Center for Robotics and Neural Systems (CRNS) is pleased to announce the talk of Dr. Séverin Lemaignan who is a senior scientist at PAL Robotics, Spain on Wednesday, March 2nd  from 11:00 am to 12:30 pm (London time) over Zoom.
>> Events: The CRNS talk series will cover a wide range of topics including social and cognitive robotics, computational neuroscience, computational linguistics, cognitive vision, machine learning, AI, and applications to autism. More details are available here:
>> Link for the next event (No Registration is Required): 

Join Zoom Meeting

>> Title of the talk:  Teaching robots autonomy in social situations

Abstract:

Participatory methodologies are now well established in social robotics to generate blueprints of what robots should do to assist humans. The actual implementation of these blueprints, however, remains a technical challenge for us, roboticists, and the end-users are not usually involved at that stage.

In two recent studies, we have however shown that, under the right conditions, robots can directly learn their behaviours from domain experts, replacing the traditional heuristic-based or plan-based robot controllers by autonomously learnt social policies. We have derived from these studies a novel 'end-to-end' participatory methodology called LEADOR, that I will introduce during the seminar.

I will also discuss recent progress on human perception and modeling in a ROS environment with the emerging ROS4HRI standard.

>> If you have any questions, please don't hesitate to contact me, 
Regards
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