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IJCNN 2023: Call for Papers #5
January 18th, 2023 
Daniela Lopez de Luise 
WCAIAA 2023 📜 Springer Conference 📔 SPSU Udaipur, India | March 18-19, 2023
January 18th, 2023 Daniela Lopez de Luise Sir Padampat Singhania University, Udaipur and Soft Computing Research Society (SCRS) India are going to organize the World Conference on Artificial Intelligence: Advances and Applications (WCAIAA 2023) in Hybrid Mode during March 18-19, 2023. The after-conference proceedings of the WCAIAA 2023 will be published in the Springer Book Series, ‘Algorithms for Intelligent Systems’.
In view of your outstanding contribution in this field, we cordially welcome you to make a submission in WCAIAA- 2023 regarding your recent work. Please also circulate this call for paper among your colleagues and research students.
For more details, please visit the conference website: https://scrs.in/conference/wcaiaa23
The topics covered (but are not limited to) at the conference are as follows:
1. Artificial Intelligence
2. Intelligent Sensor, Devices & Application
Submission Deadline: January 25, 2023
Submission Link: https://cmt3.research.microsoft.com/WCAIAA2023
20th International Conference on Content-based Multimedia Indexing, sponsored by AI4Media
January 17th, 2023 Daniela Lopez de Luise The 20th International Conference on Content-based Multimedia Indexing #CBMI2023 will take place in Orleans, France from September 20th to 23rd, 2023.
Sponsored by the AI4Media project, CBMI2023 aims at bringing together the various communities involved in all aspects of content-based multimedia indexing for retrieval, browsing, management, visualization and analytics.
Call for special session proposals
CBMI’2023 is calling for high quality Special Sessions addressing innovative research related to Artificial Intelligence for analysis and indexing of multimedia and multimodal information. The main scope of the conference is in analysis and understanding of multimedia contents including:
- Multimedia information retrieval (image, audio, video, text)
- Mobile media retrieval
- Event-based media retrieval
- Affective / emotional interaction or interfaces for multimedia retrieval
- Multimedia data mining and analytics
- Multimedia retrieval for multi-modal analytics and visualization
- Multimedia recommendation
- Multimedia verification (e.g., multi-modal fact-checking, deep fake analysis)
- Summarization, browsing, and organization of multimedia content
- Evaluation and benchmarking of multimedia retrieval systems Explanations
- Application domains: health, sustainable cities, ecology,…
More information HERE > http://cbmi2023.org/call-for-special-session/
Call for regular papers
Authors are encouraged to submit previously unpublished research papers in the broad field of content-based multimedia indexing and applications. The organisers highlight contributions addressing the main problem of search and retrieval.
This call also includes artificial intelligence in multimedia analysis, user interaction, social media indexing and retrieval. In addition, special sessions on specific technical aspects or application domains are planned, such as Multimedia for Healthcare, Explainability of AI tools in Multimedia, Physical models in Multimedia mining…
The CBMI proceedings are traditionally indexed and distributed by ACM DL. Best papers will be invited to submit extended versions of their contributions to a special issue of a leading journal in the field.
More information HERE > http://cbmi2023.org/call-for-regular-papers/
Important Dates
Deadline call for special session: 23 January 2023
Deadline call for regular papers: 12 April 2023
More information about the event at http://cbmi2023.org/
ICIP 2023 Special Session on Autonomous Vehicle Vision (AVVision)
January 17th, 2023 Daniela Lopez de Luise
Early abstract submissions are required! Send your abstract (including tentative title, abstract, author list, and corresponding author affiliation and email) to Rui Fan (rui.fan@ieee.org) before January 29, 2023! If your abstract is within the scope of our special session, we will invite you to submit a full paper (4 pages). Please note: the paper review process for Special Session papers will be handled by the TPCs, along with the Regular Paper. The important dates and paper instructions are the same as Regular Paper.
Call for Papers
Due to the recent boom in artificial intelligence technologies, there are growing expectations that fully autonomous driving may become a reality in the near future and it is expected to bring fundamental changes to our society. Fully autonomous vehicles offer great potential to improve efficiency on roads, reduce traffic accidents, increase productivity, and minimize our environmental impact in the process.
As a key component of autonomous driving, autonomous vehicle vision (AVVision) systems are typically developed based on cutting-edge computer vision, machine/deep learning, image/signal processing, and advanced sensing technologies. With recent advances in deep learning, AVVision systems have achieved compelling results. However, there still exist many challenges. For instance, the perception modules cannot perform well in poor weather and/or illumination conditions or in complex urban environments. Developing robust and all-weather visual environment perception algorithms is a popular research area that requires more attention. In addition, most perception methods are computationally-intensive and cannot run in real-time on embedded and resource-limited hardware. Therefore, fully exploiting the parallel-computing architecture, such as embedded GPUs, for real-time perception, prediction, and planning is also a hot subject that is being researched in the autonomous driving field. Furthermore, existing supervised learning approaches have achieved compelling results, but their performance is fully dependent on the quality and amount of labeled training data. Labeling such data is a time-consuming and labor-intensive process. Un/self-supervised learning approaches and domain adaptation techniques are, therefore, becoming increasingly crucial for real-world autonomous driving applications.
Research papers are solicited in, but not limited to, the following topics:
• 3D geometry reconstruction for autonomous driving;
• Driving scene understanding;
• Self-supervised/unsupervised visual environment perception;
• Driver status monitoring and human-car interfaces;
• Deep/machine learning and image analysis for autonomous vehicle perception;
• Adversarial domain adaptation for autonomous driving.
Organizers
Dr. Rui Ranger Fan, Tongji University
Dr. Wenshuo Wang, McGill University
Important Dates
Paper Submission Deadline: February 15, 2023
Paper Acceptance Notification: June 21, 2023
Final Paper Submission Deadline: July 5. 2023
Submission
Paper Submission Instruction: https://cmsworkshops.com/ICIP2023/papers.php. The review process for Special Session papers will be handled by the TPCs, along with the Regular Paper.
DeepLearn 2023 Spring: early registration February 1st
January 17th, 2023 Daniela Lopez de Luise 9th INTERNATIONAL SCHOOL ON DEEP LEARNING
DeepLearn 2023 Spring
Bari, Italy
April 3-7, 2023
https://irdta.eu/deeplearn/2023sp/
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Co-organized by:
Department of Computer Science
University of Bari “Aldo Moro”
Institute for Research Development, Training and Advice – IRDTA
Brussels/London
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Early registration: February 1st, 2023
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SCOPE:
DeepLearn 2023 Spring 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, Guimarães, Las Palmas de Gran Canaria, Luleå and Bournemouth.
Deep learning is a branch of artificial intelligence covering a spectrum of current exciting 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, health informatics, medical image analysis, recommender systems, advertising, fraud detection, robotics, games, finance, biotechnology, physics experiments, biometrics, communications, climate sciences, bioinformatics, geographic information systems, 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 23 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 2023 Spring 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 2023 Spring will take place in Bari, an important economic centre on the Adriatic Sea. The venue will be:
Department of Computer Science
University of Bari “Aldo Moro”
via Edoardo Orabona, 4
70125 Bari
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:
Vipin Kumar (University of Minnesota), Knowledge Guided Deep Learning: A Framework for Accelerating Scientific Discovery
William S. Noble (University of Washington), Deep Learning Applications in Mass Spectrometry Proteomics and Single-Cell Genomics
Emma Tolley (Swiss Federal Institute of Technology Lausanne), Physics-Informed Deep Learning
PROFESSORS AND COURSES:
Babak Ehteshami Bejnordi (Qualcomm AI Research), [intermediate/advanced] Conditional Computation for Efficient Deep Learning with Applications to Computer Vision, Multi-Task Learning, and Continual Learning
Patrick Gallinari (Sorbonne University), [intermediate] Physics Aware Deep Learning for Modeling Dynamical Systems
Sergei V. Gleyzer (University of Alabama), [introductory/intermediate] Machine Learning Fundamentals and Their Applications to Very Large Scientific Data: Rare Signal and Feature Extraction, End-to-End Deep Learning, Uncertainty Estimation and Realtime Machine Learning Applications in Software and Hardware
Jacob Goldberger (Bar-Ilan University), [introductory/intermediate] Calibration Methods for Neural Networks
Christoph Lampert (Institute of Science and Technology Austria), [intermediate] Training with Fairness and Robustness Guarantees
Yingbin Liang (Ohio State University), [intermediate/advanced] Bilevel Optimization and Applications in Deep Learning
Miaoyuan Liu (Purdue University), [introductory/intermediate] Edge of the Future: AI in Real Time Systems of Scientific Instruments
Xiaoming Liu (Michigan State University), [intermediate] Deep Learning for Trustworthy Biometrics
Michael Mahoney (University of California Berkeley), [intermediate] Practical Neural Network Theory
Liza Mijovic (University of Edinburgh), [introductory/intermediate] Deep Learning & the Higgs Boson: Classification with Fully Connected and Adversarial Networks
Bhiksha Raj (Carnegie Mellon University), [introductory] An Introduction to Quantum Neural Networks [with Rita Singh and Daniel Justice]
Holger Rauhut (RWTH Aachen University), [intermediate] Gradient Descent Methods for Learning Neural Networks: Convergence and Implicit Bias
Bart ter Haar Romeny (Eindhoven University of Technology), [intermediate/advanced] Explainable Deep Learning from First Principles
Tara Sainath (Google), [advanced] E2E Speech Recognition
Martin Schultz (Research Centre Jülich), [introductory/intermediate] Deep Learning for Air Quality, Weather and Climate
Hao Su (University of California San Diego), [intermediate/advanced] Neural Representation for 3D Capturing
Adi Laurentiu Tarca (Wayne State University), [intermediate] Machine Learning for Cross-Sectional and Longitudinal Omics Studies
Zhi Tian (George Mason University), [intermediate] Communication-Efficient and Robust Distributed Learning
Michalis Vazirgiannis (Polytechnic Institute of Paris), [intermediate/advanced] Graph Machine Learning with GNNs and Applications
Atlas Wang (University of Texas Austin), [intermediate] Sparse Neural Networks: From Practice to Theory
Guo-Wei Wei (Michigan State University), [introductory/advanced] Discovering the Mechanisms of SARS-CoV-2 Evolution and Transmission
Lei Xing (Stanford University), [intermediate] Deep Learning for Medical Imaging and Genomic Data Processing: from Data Acquisition, Analysis, to Biomedical Applications
Xiaowei Xu (University of Arkansas Little Rock), [intermediate/advanced] Deep Learning Language Models and Causal Inference
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 March 26, 2023.
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 March 26, 2023.
EMPLOYER SESSION:
Organizations 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 March 26, 2023.
ORGANIZING COMMITTEE:
Giuseppina Andresini (Bari, local co-chair)
Graziella De Martino (Bari, local co-chair)
Corrado Loglisci (Bari, local co-chair)
Donato Malerba (Bari, local chair)
Carlos Martín-Vide (Tarragona, program chair)
Paolo Mignone (Bari, local co-chair)
Sara Morales (Brussels)
Gianvito Pio (Bari, local co-chair)
Francesca Prisciandaro (Bari, local co-chair)
David Silva (London, organization chair)
Gennaro Vessio (Bari, local co-chair)
REGISTRATION:
It has to be done at
https://irdta.eu/deeplearn/2023sp/registration/
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 are available at
https://irdta.eu/deeplearn/2023sp/accommodation/
CERTIFICATE:
A certificate of successful participation in the event will be delivered indicating the number of hours of lectures.
QUESTIONS AND FURTHER INFORMATION:
ACKNOWLEDGMENTS:
University of Bari “Aldo Moro”
Rovira i Virgili University
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