AI4Media: Online survey on AI Technology for Image & Video Organization

March 9th, 2022 Daniela Lopez de Luise
The AI4Media H2020 project launches an online survey to collect the 
opinions of the AI research community and media sector representatives 
with regard to the use of AI technologies for Image & Video 
Organization.

The AI4Media project focuses on delivering the next generation of core 
AI advances and training to serve the media sector. The survey is 
organized by Imagga, a member of the AI4Media consortium.

About the survey

The survey will help the AI4Media consortium in harmonizing AI 
research with industrial needs. The results of this survey will be 
published as a white paper, part of AI4Media's public deliverable 
“Initial Pilot Evaluation Report and Synchronizing AI Research with 
Industrial Needs”.

The goal of the survey is to assess and evaluate the need for 
automated (re) organization of large media collections of photos and 
video as well as to explore the potential market for professional 
functionalities and tools for better image and video organization of 
the content of media companies.

The survey is anonymous, so there is no need to give your name or the 
name of your organization. Data is not linked to respondents and is 
only going to be used in an aggregated way in this deliverable or any 
subsequent AI4Media publications. No personal data of the survey 
respondents will be collected or stored.

The survey takes around 5-10 minutes to complete.

FILL IN THE SURVEY

until Wednesday, 23 March 2022, 23:59 CET!

Your opinion is important and would help us a lot!

In case of questions about this survey, please send an email to 
chris@imagga.com.

Kind regards,

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CVPR 2022 Mobile AI workshop and challenges

March 9th, 2022 Daniela Lopez de Luise
CALL FOR PAPERS  & CALL FOR PARTICIPANTS IN CHALLENGES
2nd Mobile AI workshop and challenges on
learned ISP, denoising, HDR, image/video super-resolution, depth estimation, validated on mobile hardware
In conjunction with CVPR 2022, 20th of June, New Orleans, USA.
TOPICS
● Efficient deep learning models for mobile devices
● Artifacts removal from mobile photos/videos
● General smartphone photo/video enhancement
● RAW camera image/video processing
● Deep learning applications for mobile camera ISPs
● Image/video super-resolution on low-power hardware
● Portrait segmentation / bokeh effect rendering
● Depth estimation w/o multiple cameras
● Perceptual image manipulation on mobile devices
● Activity recognition using smartphone sensors
● Image/sensor based identity recognition
● Fast image classification / object detection algorithms
● NLP models optimized for mobile inference
● Real-time semantic segmentation
● Low-power machine learning inference
● Machine learning and deep learning frameworks for mobile devices
● AI performance evaluation / benchmarking of mobile and IoT hardware
● Studies and applications of the above problems

SUBMISSION
A paper submission has to be in English, in pdf format, and at most 8 pages (excluding references) in CVPR style. 
The review process is double blind.
Accepted and presented papers will be published in the 2022 CVPR Workshops Proceedings by IEEE and CVF.

Author Kit: https://cvpr2022.thecvf.com/sites/default/files/2021-10/cvpr2022-author_kit-v1_1-1.zip 

WORKSHOP DATES
● Paper submission deadline: March 20, 2022
● Paper decision notification: April 14, 2022

CHALLENGES (TBU)
● Learned Smartphone ISP
● Image Denoising
● HDR Image Processing
● Image Super-Resolution
● Video Super-Resolution
● Depth Estimation
To learn more about the challenges, to participate in the challenges, and to access the data everybody is invited to check the Mobile AI 2022 web page:
https://ai-benchmark.com/workshops/mai/2022/

For those interested in restoration, enhancement, manipulation, super-resolution without specific mobile hardware constraints we refer to the CVPR22 NTIRE Workshop and Challenges:
CHALLENGES DATES (TBU)

● Release of train data: March 21, 2022
● Competitions end: May 31, 2022

Website: https://ai-benchmark.com/workshops/mai/2022/

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CFP: MLJ special issue on Foundations of Data Science (new submission deadline: 1 April 2022)

March 9th, 2022 Daniela Lopez de Luise
Data science is a hot topic with an extensive scope, both in terms of  theory and applications. Machine Learning forms one of its core foundational pillars.
Simultaneously, Data Science applications provide important challenges that can often be addressed only with innovative Machine Learning algorithms and methodologies. This special issue will highlight the latest development of the Machine Learning foundations of data science and on the synergy of data science and machine learning. We welcome new developments in statistics, mathematics, informatics and computing-driven machine learning for data science, including foundations, algorithms and models, systems, innovative applications and other research contributions.

Following the great success of the 2021 MLJ special issue with DSAA'2021, this
2022 special issue will further capture the state-of-the-art machine learning advances for data science. Accepted papers will be published in MLJ and presented at a journal track of the 2022 IEEE International Conference on Data Science and Advanced Analytics (DSAA'2022) in Shenzhen, October 2022.

====================
Topics of Interest
====================
We welcome original and well-grounded research papers on all aspects of foundations of data science including but not limited to the following topics:

Machine Learning Foundations for Data Science
* Auto-ML
* Information fusion from disparate sources
* Feature engineering, embedding, mining and representation
* Learning from network and graph data
* Learning from data with domain knowledge
* Reinforcement learning
* Non-IID learning, nonstationary, coupled and entangled learning
* Heterogeneous, mixed, multimodal, multi-view and multi-distributional learning
* Online, streaming, dynamic and real-time learning
* Causality and learning causal models
* Multi-instance, multi-label, multi-class and multi-target learning
* Semi-supervised and weakly supervised learning
* Representation learning of complex interactions, couplings, relations
* Deep learning theories and models
* Evaluation of data science systems
* Open domain/set learning

Emerging Impactful Machine Learning Applications
* Data preprocessing, manipulation and augmentation
* Autonomous learning and optimization systems
* Digital, social, economic and financial (finance, FinTech, blockchains and
cryptocurrencies) analytics
* Graph and network embedding and mining
* Machine learning for recommender systems, marketing, online and e-commerce
* Augmented reality, computer vision and image processing
* Risk, compliance, regulation, anomaly, debt, failure and crisis
* Cybersecurity and information disorder, misinformation/fake detection
* Human-centered and domain-driven data science and learning
* Privacy, ethics, transparency, accountability, responsibility, trust, reproducibility and retractability
* Fairness, explainability and algorithm bias
* Green and energy-efficient, scalable, cloud/distributed and parallel analytics and infrastructures
* IoT, smart city, smart home, telecommunications, 5G and mobile data science and learning
* Government and enterprise data science
* Transportation, manufacturing, procurement, and Industry 4.0
* Energy, smart grids and renewable energies
* Agricultural, environmental and spatio-temporal analytics and climate change

Contributions must contain new, unpublished, original and fundamental work relating to the Machine Learning Journal's mission. All submissions will be reviewed using rigorous scientific criteria whereby the novelty of the contribution will be crucial.

====================
Submission Instructions
====================
Submit manuscripts to: http://MACH.edmgr.com.  Select this special issue as the article type. Papers must be prepared in accordance with the Journal guidelines:
https://www.springer.com/journal/10994

All papers will be reviewed following standard reviewing procedures for the Journal.

====================
Key Dates
====================
We will have a continuous submission/review process starting in Oct. 2021.

Last paper submission deadline: 1 April 2022

Paper acceptance: 1 June 2022

Camera-ready: 15 June 2022

====================
Guest Editors
====================
Longbing Cao, University of Technology Sydney, Australia

João Gama, University of Porto, Portugal

Nitesh Chawla, University of Notre Dame, United States

Joshua Huang, Shenzhen University, China

Carlos Ferreira

ISEP | Instituto Superior de Engenharia do Porto Rua Dr. António Bernardino de Almeida, 431
4249-015 Porto – PORTUGAL
tel. +351 228 340 500 | fax +351 228 321 159 mail@isep.ipp.pt | www.isep.ipp.pt

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Submission deadline is extended! Get top publication opportunities and increased visibility for your research with EAI

March 9th, 2022 Daniela Lopez de Luise

Web version
Forward

EAI COMPSE 2022 – 6th EAI International Conference on Computer Science and Engineering 

New Conference Date: August 25, 2022 | Mexico City, Mexico 
Submission Deadline: 30 May 2022

SCOPE

DIGITAL TRANSFORMATION IN ORGANIZATIONS: NEW CHALLENGES IN THE POST-COVID ERA

The COVID-19 pandemic has impacted the world in every way and companies have begun to review their strategies, however, the challenges that this crisis is presenting provides an opportunity for organizations to evolve to a new reality where digital predominates. Although the digital transformation process has been taking place in the country in recent years, COVID-19 has further accelerated the pace of change and the speed of transformation and this has forced the laggards to accelerate the pace to match the digital leaders. In this period of time, companies must respond without losing sight of recovery and preparation for the future.

For this reason, COMPSE 2022 invites professionals, researchers, students and practitioners to submit work related to new strategies to optimize the operation of companies in the post-covid era.

We are pleased to invite you to submit your paper to EAI COMPSE 2022. Submissions should be in English, following the Springer formatting guidelines (see Submission). 

Submit Paper

Read more: Call for Papers

Publication

All registered papers will be submitted for publishing by Springer and made available through SpringerLink Digital Library: COMPSE Conference Proceedings.

Proceedings will be submitted for inclusion in leading indexing services, such as Web of Science, Compendex, Scopus, DBLP, EU Digital Library, Google Scholar, IO-Port, MathSciNet, Inspec, and Zentralblatt MATH.

Authors of selected papers will be invited to submit an extended version to:

All accepted authors are eligible to submit an extended version in a fast track of:

Additional publication opportunities:

Important dates

Full Paper Submission Deadline: 30 May 2022

Notification Deadline: 28 June 2022

Camera-ready deadline: 28 July 2022

Conference dates: 25 August 2022

ORGANIZING COMMITTEE

General Chair

Jose Antonio Marmolejo, Universidad Panamericana Mexico, Mexico

Brenda María Retana Blanco, Universidad Anáhuac Mexico, Mexico

Utku Köse, Suleyman Demirel University, Turkey

Pandian Vasant, Ton Duc Thang University, Vietnam

Technical Program Committee Chairs

Roman Rodriguez Aguilar, Universidad Panamericana Mexico, Mexico

Igor Litvinchev, Universidad Autonoma de Nuevo Leon, Mexico

See the full Organizing Committee and Technical Program Committee. 

keynote speaker

Dr. Utku Kose

Suleyman Demirel University, Turkey

Title: Blurred Technology: Roadmaps for Artificial Intelligence and Bioengineering
Read full Bio & Abstract

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DeepLearn 2022 Summer: early registration April 1st

March 9th, 2022 Daniela Lopez de Luise
6th INTERNATIONAL GRAN CANARIA SCHOOL ON DEEP LEARNING
DeepLearn 2022 Summer
Las Palmas de Gran Canaria, Spain
July 25-29, 2022
*****************
Co-organized by:
University of Las Palmas de Gran Canaria
Institute for Research Development, Training and Advice – IRDTA
Brussels/London
******************************************************************
Early registration: April 1st, 2022
******************************************************************
SCOPE:
DeepLearn 2022 Summer will be a research training event with a global scope aiming at updating participants on the most recent advances in the critical and fast developing area of deep learning. Previous events were held in Bilbao, Genova, Warsaw, Las Palmas de Gran Canaria, Bournemouth, and Guimarães.
Deep learning is a branch of artificial intelligence covering a spectrum of current frontier research and industrial innovation that provides more efficient algorithms to deal with large-scale data in a huge variety of environments: computer vision, neurosciences, speech recognition, language processing, human-computer interaction, drug discovery, biomedical informatics, image analysis, recommender systems, advertising, fraud detection, robotics, games, finance, biotechnology, physics experiments, biometrics, communications, climate sciences, etc. etc. Renowned academics and industry pioneers will lecture and share their views with the audience.
Most deep learning subareas will be displayed, and main challenges identified through 24 four-hour and a half courses and 3 keynote lectures, which will tackle the most active and promising topics. The organizers are convinced that outstanding speakers will attract the brightest and most motivated students. Face to face interaction and networking will be main ingredients of the event. It will be also possible to fully participate in vivo remotely.
An open session will give participants the opportunity to present their own work in progress in 5 minutes. Moreover, there will be two special sessions with industrial and recruitment profiles.
ADDRESSED TO:
Graduate students, postgraduate students and industry practitioners will be typical profiles of participants. However, there are no formal pre-requisites for attendance in terms of academic degrees, so people less or more advanced in their career will be welcome as well. Since there will be a variety of levels, specific knowledge background may be assumed for some of the courses. Overall, DeepLearn 2022 Summer is addressed to students, researchers and practitioners who want to keep themselves updated about recent developments and future trends. All will surely find it fruitful to listen to and discuss with major researchers, industry leaders and innovators.
VENUE:
DeepLearn 2022 Summer will take place in Las Palmas de Gran Canaria, on the Atlantic Ocean, with a mild climate throughout the year, sandy beaches and a renowned carnival. The venue will be:
Institución Ferial de Canarias
Avenida de la Feria, 1
35012 Las Palmas de Gran Canaria
STRUCTURE:
3 courses will run in parallel during the whole event. Participants will be able to freely choose the courses they wish to attend as well as to move from one to another.
Full live online participation will be possible. However, the organizers highlight the importance of face to face interaction and networking in this kind of research training event.
KEYNOTE SPEAKERS:
Wahid Bhimji (Lawrence Berkeley National Laboratory), Deep Learning on Supercomputers for Fundamental Science
Joachim M. Buhmann (Swiss Federal Institute of Technology Zurich), Machine Learning — A Paradigm Shift in Human Thought!?
Kate Saenko (Boston University), Overcoming Dataset Bias in Deep Learning
PROFESSORS AND COURSES:
Tülay Adalı (University of Maryland Baltimore County), [intermediate] Data Fusion Using Matrix and Tensor Factorizations
Pierre Baldi (University of California Irvine), [intermediate/advanced] Deep Learning: From Theory to Applications in the Natural Sciences
Arindam Banerjee (University of Illinois Urbana-Champaign), [intermediate/advanced] Deep Generative and Dynamical Models
Mikhail Belkin (University of California San Diego), [intermediate/advanced] Modern Machine Learning and Deep Learning through the Prism of Interpolation
Dumitru Erhan (Google), [intermediate/advanced] Visual Self-supervised Learning and World Models
Arthur Gretton (University College London), [intermediate/advanced] Probability Divergences and Generative Models
Phillip Isola (Massachusetts Institute of Technology), [intermediate] Deep Generative Models
Mohit Iyyer (University of Massachusetts Amherst), [intermediate/advanced] Natural Language Generation
Irwin King (Chinese University of Hong Kong), [intermediate/advanced] Deep Learning on Graphs
Vincent Lepetit (Paris Institute of Technology), [intermediate] Deep Learning and 3D Reasoning for 3D Scene Understanding
Yan Liu (University of Southern California), [introductory/intermediate] Deep Learning for Time Series
Dimitris N. Metaxas (Rutgers, The State University of New Jersey), [intermediate/advanced] Model-based, Explainable, Semisupervised and Unsupervised Machine Learning for Dynamic Analytics in Computer Vision and Medical Image Analysis
Sean Meyn (University of Florida), [introductory/intermediate] Reinforcement Learning: Fundamentals, and Roadmaps for Successful Design
Louis-Philippe Morency (Carnegie Mellon University), [intermediate/advanced] Multimodal Machine Learning
Wojciech Samek (Fraunhofer Heinrich Hertz Institute), [introductory/intermediate] Explainable AI: Concepts, Methods and Applications
Clara I. Sánchez (University of Amsterdam), [introductory/intermediate] Mechanisms for Trustworthy AI in Medical Image Analysis and Healthcare
Björn W. Schuller (Imperial College London), [introductory/intermediate] Deep Multimedia Processing
Jonathon Shlens (Apple), [introductory/intermediate] An Introduction to Computer Vision and Convolution Neural Networks
Johan Suykens (KU Leuven), [introductory/intermediate] Deep Learning, Neural Networks and Kernel Machines
Csaba Szepesvári (University of Alberta), [intermediate/advanced] Tools and Techniques of Reinforcement Learning to Overcome Bellman's Curse of Dimensionality

Murat Tekalp (Koç University), [intermediate/advanced] Deep Learning for Image/Video Restoration and Compression

Alexandre Tkatchenko (University of Luxembourg), [introductory/intermediate] Machine Learning for Physics and Chemistry
Li Xiong (Emory University), [introductory/intermediate] Differential Privacy and Certified Robustness for Deep Learning
Ming Yuan (Columbia University), [intermediate/advanced] Low Rank Tensor Methods in High Dimensional Data Analysis
OPEN SESSION:
An open session will collect 5-minute voluntary presentations of work in progress by participants. They should submit a half-page abstract containing the title, authors, and summary of the research to david@irdta.eu by July 17, 2022.
INDUSTRIAL SESSION:
A session will be devoted to 10-minute demonstrations of practical applications of deep learning in industry. Companies interested in contributing are welcome to submit a 1-page abstract containing the program of the demonstration and the logistics needed. People in charge of the demonstration must register for the event. Expressions of interest have to be submitted to david@irdta.eu by July 17, 2022.
EMPLOYER SESSION:
Firms searching for personnel well skilled in deep learning will have a space reserved for one-to-one contacts. It is recommended to produce a 1-page .pdf leaflet with a brief description of the company and the profiles looked for to be circulated among the participants prior to the event. People in charge of the search must register for the event. Expressions of interest have to be submitted to david@irdta.eu by July 17, 2022.
ORGANIZING COMMITTEE:
Marisol Izquierdo (Las Palmas de Gran Canaria, local chair)
Carlos Martín-Vide (Tarragona, program chair)
Sara Morales (Brussels)
David Silva (London, organization chair)
REGISTRATION:
It has to be done at
The selection of 8 courses requested in the registration template is only tentative and non-binding. For the sake of organization, it will be helpful to have an estimation of the respective demand for each course. During the event, participants will be free to attend the courses they wish.
Since the capacity of the venue is limited, registration requests will be processed on a first come first served basis. The registration period will be closed and the on-line registration tool disabled when the capacity of the venue will have got exhausted. It is highly recommended to register prior to the event.
FEES:
Fees comprise access to all courses and lunches. There are several early registration deadlines. Fees depend on the registration deadline. The fees for on site and for online participation are the same.
ACCOMMODATION:
Accommodation suggestions will be available in due time at
CERTIFICATE:
A certificate of successful participation in the event will be delivered indicating the number of hours of lectures.
QUESTIONS AND FURTHER INFORMATION:
ACKNOWLEDGMENTS:
Cabildo de Gran Canaria
Universidad de Las Palmas de Gran Canaria
Universitat Rovira i Virgili
Institute for Research Development, Training and Advice – IRDTA, Brussels/London
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