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. 

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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/

***********

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:

david@irdta.eu

ACKNOWLEDGMENTS:

University of Bari “Aldo Moro”

Rovira i Virgili University

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CfP – Workshop on Machine Learning for Streaming Media (ML4SM) at The WebConf2023

January 17th, 2023 Daniela Lopez de Luise
Call for Papers: Workshop on Machine Learning for Streaming Media at The WebConf2023

Austin, Texas, USA, Sunday, April 30, 2023

https://ml4streamingmedia-workshop.github.io/www/index.html 

 

Streaming media have been seeing massive year over year growth in terms of consumption hours recently. For many people, streaming services have become part of everyday life and accessing and consuming media content via streaming is now the norm for people of all ages. Powered by Machine Learning (ML) algorithms, streaming services are becoming one the most visible and impactful applications of ML that directly interact with people and influence their lives.

 

Despite the rapid growth of streaming services, the research discussions around ML for streaming media remain fragmented across different conferences and workshops. Also, the gap between academic research and constraints and requirements in industry limits the broader impact of many contributions from academia. Therefore, we believe that there is an urgent need to: (i) build connections and bridge the gap by bringing together researchers and practitioners from both academia and industry working on these problems, (ii) attract ML researchers from other areas to streaming media problems, and (iii) bring up the pain points and battle scars in industry to which academia researchers can pay more attention.

 

With this motivation in mind, we are organizing a workshop on Machine Learning for Streaming Media in conjunction with the WebConf 2023. We invite quality research contributions, including original research, preliminary research results, and proposals for new work, to be submitted. All submitted papers will be peer reviewed by the program committee and judged for their relevance to the workshop, especially to the topics identified below, and their potential to generate discussion. Accepted submissions will be presented at the workshop and will be published in the companion (workshop) proceedings of the WebConf 2023. We welcome research that has been previously published or is under review elsewhere. Such articles should be clearly identified at the time of submission and will not be published in the proceedings.

 

Workshop Topics

 

The main topics we would like to consider for this workshop are

· Content Understanding

o   Multimodal representation

o   Feature extraction for audio, video, and image content

o   Knowledge Graph generation for streaming media

o   Semi-supervised learning for content understanding

o   Metadata enrichment for music, podcast, video catalog

· Search and recommendation for streaming media

o   Named entity recognition (e.g. identifying celebrities, hosts, artists)

o   Conversational systems

o   Reward modeling and shaping

o   Item cold start problems and challenges

o   Designing scalable ML systems

o   Heterogeneous content recommendation

o   Learning to rank

o   Transfer learning

o   Explainable recommendations

o   Representation learning

o   Graph learning algorithms for streaming media

· Measurement, Metrics & Evaluation

o   Evaluation methodologies for streaming media search and recommendations

o   Methodologies for valuation of content

o   Measuring business impact of recommendation systems

o   Life-time value modeling

o   Churn prediction & retention modeling

· User Studies & Human-In the Loop

o   User studies on real-world recommenders – Human-In the loop recommendations

o   Mixed methods research

o   User studies on preference elicitation

· Trust, Safety & Algorithmic Fairness

o   Identifying misinformation and disinformation – Algorithmic fairness in recommendations

o   Hate-speech and fake news detection

o   Content moderation

o   Societal impact of recommendation systems for streaming media

· Machine learning to optimize streaming quality of experience

Important Dates

· Submission deadline: 6th of February 2023                

· Author notification: 6th of March 2023

· Camera-ready version deadline: 20th of March 2023

· Workshop: Either 1st of May OR 2nd of May 2023    

All deadlines are 11:59 pm, Anywhere on Earth (AoE).

 

Submission Instructions

 

 

Formatting Instructions

 

Submissions should not exceed six pages in length (including appendices and references). Papers must be submitted in PDF format according to the ACM template published in the ACM guidelines, selecting the generic “sigconf” sample. The PDF files must have all non-standard fonts embedded. Workshop papers must be self-contained and in English.

 

Registration and Attendance

 

Further, at least one author of each accepted workshop paper has to register for the main conference. Workshop attendance is only granted for registered participants.

 

Workshop Organizers

· Sudarshan Lamkhede – Manager, Machine Learning – Search and Recommendations, Netflix Research. 

· Praveen Chandar – Staff Research Scientist, Spotify 

· Vladan Radosavljevic – Machine Learning Engineering Manager, Spotify 

· Amit Goyal – Senior Applied Scientist, Amazon Music

· Lan Luo – Associate Professor of Marketing, University of Southern California

If you have any questions please do not hesitate to reach out to the workshop organizers via organizers-ml4sm at googlegroups dot com

 

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ICMI 2023 – Call for Papers

January 17th, 2023 Daniela Lopez de Luise
25th ACM International Conference on Multimodal Interaction (ICMI 2023)

9-13 October 2023, Paris, France

 

The 25th International Conference on Multimodal Interaction (ICMI 2023) will be held in Paris, France. ICMI is the premier international forum that brings together multimodal artificial intelligence (AI) and social interaction research. Multimodal AI encompasses technical challenges in machine learning and computational modeling such as representations, fusion, data and systems. The study of social interactions englobes both human-human interactions and human-computer interactions. A unique aspect of ICMI is its multidisciplinary nature which values both scientific discoveries and technical modeling achievements, with an eye towards impactful applications for the good of people and society.

 

ICMI 2023 will feature a single-track main conference which includes:

keynote speakers, technical full and short papers (including oral and poster presentations), demonstrations, exhibits, doctoral consortium, and late-breaking papers. The conference will also feature tutorials, workshops and grand challenges. The proceedings of all ICMI 2023 papers, including Long and Short Papers, will be published by ACM as part of their series of International Conference Proceedings and Digital Library, and the adjunct proceedings will feature the workshop papers.

 

Novelty will be assessed along two dimensions: scientific novelty and technical novelty. Accepted papers at ICMI 2023 will need to be novel along one of the two dimensions:

* Scientific Novelty: Papers should bring new scientific knowledge about human social interactions, including human-computer interactions. For example, discovering new behavioral markers that are predictive of mental health or how new behavioral patterns relate to children’s interactions during learning. It is the responsibility of the authors to perform a proper literature review and clearly discuss the novelty in the scientific discoveries made in their paper.

* Technical Novelty: Papers should propose novelty in their computational approach for recognizing, generating or modeling multimodal data. Examples

include: novelty in the learning and prediction algorithms, in the neural architecture, or in the data representation. Novelty can also be associated with new usages of an existing approach.

 

Please see the Submission Guidelines for Authors https://icmi.acm.org/ for detailed submission instructions. Commitment to ethical conduct is required and submissions must adhere to ethical standards in particular when human-derived data are employed. Authors are encouraged to read the ACM Code of Ethics and Professional Conduct (https://ethics.acm.org/).

 

ICMI 2023 conference theme: The theme for this year’s conference is “Science of Multimodal Interactions”. As the community grows, it is important to understand the main scientific pillars involved in deep understanding of multimodal social interactions. As a first step, we want to acknowledge key discoveries and contributions that the ICMI community enabled over the past

20+ years. As a second step, we reflect on the core principles,

20+ foundational

methodologies and scientific knowledge involved in studying and modeling multimodal interactions. This will help establish a distinctive research identity for the ICMI community while at the same time embracing its multidisciplinary collaborative nature. This research identity and long-term agenda will enable the community to develop future technologies and applications while maintaining commitment to world-class scientific research.

Additional topics of interest include but are not limited to:

* Affective computing and interaction

* Cognitive modeling and multimodal interaction

* Gesture, touch and haptics

* Healthcare, assistive technologies

* Human communication dynamics

* Human-robot/agent multimodal interaction

* Human-centered A.I. and ethics

* Interaction with smart environment

* Machine learning for multimodal interaction

* Mobile multimodal systems

* Multimodal behaviour generation

* Multimodal datasets and validation

* Multimodal dialogue modeling

* Multimodal fusion and representation

* Multimodal interactive applications

* Novel multimodal datasets

* Speech behaviours in social interaction

* System components and multimodal platforms

* Visual behaviours in social interaction

* Virtual/augmented reality and multimodal interaction

 

Important Dates

Paper Submission: May 1, 2023

Rebuttal period: June 26-29, 2023

Paper notification: July 21, 2023

Camera-ready paper: August 14, 2023

Presenting at main conference: October 9-13, 2023

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LAST CALL for the IET Image Processing special issue on “Advancements in Fine Art Pattern Extraction and Recognition” (deadline 31 January 2023)

January 17th, 2023 Daniela Lopez de Luise
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Aim & Scope

Cultural heritage, especially fine arts, plays an invaluable role in the
cultural, historical and economic growth of our societies. Fine arts are
primarily developed for aesthetic purposes and are mainly expressed
through painting, sculpture and architecture. In recent years, thanks to
technological improvements and drastic cost reductions, a large-scale
digitization effort has been made, which has led to an increasing
availability of large digitized fine art collections. This availability,
coupled with recent advances in pattern recognition and computer vision,
has disclosed new opportunities, especially for researchers in these
fields, to assist the art community with automatic tools to further
analyze and understand fine arts. Among other benefits, a deeper
understanding of fine arts has the potential to make them more
accessible to a wider population, both in terms of fruition and
creation, thus supporting the spread of culture.

This special issue aims to offer the opportunity to present advancements
in the state-of-the-art, innovative research, ongoing projects, and
academic and industrial reports on the application of visual pattern
extraction and recognition for a better understanding and fruition of
fine arts, soliciting contributions from pattern recognition, computer
vision, artificial intelligence and image processing research areas. The
special issue will be linked to the 2nd International Workshop on Fine
Art Pattern Extraction and Recognition (FAPER2022). Authors of selected
conference papers will be invited to extend and improve their
contributions for this special issue, and authors are also invited to
submit new contributions (non-conference papers).

_______________________________________
Topics include, but are not limited to:
– Applications of machine learning and deep learning to cultural
heritage and digital humanities
– Computer vision and multimedia data processing for fine arts
– Generative adversarial networks for artistic data
– Augmented and virtual reality for cultural heritage
– 3D reconstruction of historical artifacts
– Point cloud segmentation and classification for cultural heritage
– Historical document analysis
– Content-based retrieval in visual art domain
– Digitally enriched museum visits
– Smart interactive experiences in cultural sites
– Project, products or prototypes for cultural heritage

_______________________________________________
Submission Deadline (extended): 31 January 2023

Submissions must be made through ScholarOne:
https://mc.manuscriptcentral.com/theiet-ipr

see the PDF call for paper for more information:
https://ietresearch.onlinelibrary.wiley.com/pb-assets/assets/17519667/Special%20Issues/IPR%20SI%20CFP_AFAPER-1651107571727.pdf

___________
Open Access

 From January 2021, The IET began an Open Access publishing partnership
with Wiley. As a result, all submissions that are accepted for this
Special Issue will be published under the Gold Open Access Model and
subject to the Article Processing Charge (APC) of $2,300.

*APC can be covered in FULL, i.e. FREE OF CHARGE*, or part by your
institution
*CHECK  YOUR  ELIGIBILITY  HERE*
https://authorservices.wiley.com/author-resources/Journal-Authors/open-access/affiliation-policies-payments/institutional-funder-payments.html

_______________
Editor-in-Chief

Prof. Farzin Deravi, University of Kent, UK

_____________
Guest Editors

Giovanna Castellano, Universita' di Bari, Italy
Gennaro Vessio, Universita' di Bari, Italy
Fabio Bellavia, Universita' di Palermo, Italy
Sinem Aslan, Università Ca' Forscari Venezia, Italy

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