ECCV 2022 AIMIA workshop: Digital Pathology & Radiology/COVID19 :: Call for Papers [EXTENDED DEADLINE]

July 6th, 2022 Daniela Lopez de Luise

The ECCV 2022 workshop on AI-enabled Medical Image Analysis (AIMIA) aims at providing a platform for scientific discussion and presentation of ideas to tackle the challenges of whole slide image

and CT/MRI/X-ray analysis/processing and identify research opportunities in the context of Digital Pathology and Radiology/COVID19.

AIMIA is jointly organised by INESCTEC (Portugal), NTUA (Greece), IMP Diagnostics (Portugal), Radboudumc (The Netherlands), Karolinska Institutet (Sweden), Google Health (USA) and the University

of Lincoln (UK). For more information please visit http://vcmi.inesctec.pt/aimia_eccv

***** IMPORTANT DATES *****

Submission deadline: July 15, 2022

Author notification: August 05, 2022

Camera-ready deadline: August 12, 2022 

AIMIA workshop: October 2022 (T.B.D.) 

***** KEYNOTE SPEAKERS *****

Dimitri Metaxas, Rutgers University, USA

Inti Zlobec, University of Bern, Switzerland

Henning Müller, HES-SO Vallais-Wallis, Switzerland

***** TOPICS OF INTEREST *****

The AIMIA workshop welcomes works that focus on (but are not limited to):


  • Semi-/weakly-/self-supervised learning methodologies;

  • Detection, classification and segmentation;

  • Disease diagnosis, grading and prognosis;

  • Treatment response prediction;

  • Detection of tissue biomarkers with predictive/prognostic value;

  • Image registration;

  • Explainable AI;

  • Clinical applications;

 

applied to Digital Pathology (TRACK A) and Radiology/COVID19 (TRACK B).

The workshop also invites submissions to the 2nd COV19D competition, organized within TRACK B: https://mlearn.lincoln.ac.uk/eccv-2022-ai-mia/


***** PAPER SUBMISSION *****

Submitted manuscripts should be anonymised and formatted according to the ECCV style, with a maximum of 14 pages, including images and tables and excluding cited references.

Accepted papers will be published in Springer, as part of the ECCV 2022 proceedings (workshops set).

Do you want to submit your work? Please access https://cmt3.research.microsoft.com/AIMIA2022.

***** CONTACTS *****

Sara P. Oliveira (sara.i.oliveira@inesctec.pt)

Jaime Cardoso (jaime.cardoso@inesctec.pt)

Stefanos Kollias (stefanos@cs.ntua.gr)
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CFP Vehicle Sensing and Monitorization at ECCV 2022 – Upcoming Deadline

July 6th, 2022 Daniela Lopez de Luise 
Call for Papers --------------- ISM 2022 - THE FIRST IN-VEHICLE SENSING AND MONITORIZATION WORKSHOP
to be held in conjunction with ECCV 2022 - European Conference on Computer Vision 2022 Tel Aviv (Israel), October 23-24, 2022
 Links ----- Workshop: https://ism.inesctec.pt/ ECCV 2022: https://eccv2022.ecva.net/
 Important Dates ----------------- Submission date: July 7th, 2022 Notification date: August 1st, 2022 Camera ready: August 8th, 2022
 Workshop Motivation and Topics ----------------------------------- Driver assistance and autonomous driving technologies have made significant progress over the past decade. Much of the research has been devoted to monitoring the external environment, while not nearly as much attention has been paid to the interior.
Interior monitoring increases safety, comfort, and convenience for all vehicle occupants, especially in the case of autonomous shared vehicles.
The In-vehicle Sensing and Monitorization workshop at ECCV 2022 targets the processing of data collected inside the vehicle for monitoring and event detection. It covers topics such as activity detection, emotional monitoring, identification of undesired behavior, damage detection, and many others related to the automatic supervision of the interior of shared vehicles and its occupants.
We invite contributions that address themes related to In-Vehicle Sensing and Monitoring.
Topics include but are not limited to:  * Detection and prediction of degraded driver state, including inattention, fatigue, cognitive load, intoxication, and sudden illness;  * Driver activity recognition, including mobile phone use, eating, applying makeup/grooming, and interacting with passengers;  * Situation-dependent and personalized driver state detection and activity recognition;  * Driver/occupant intention prediction;  * Activity recognition and emotional monitoring;  * Identification of undesired behaviors and damage detection;  * Image segmentation for passenger detection;  * Driver and passenger biometrics;  * Body gestures and pose estimation;  * Human activity recording, simulation, and generation (i.e., database acquisition, synthetic data) for vehicle interior scenarios;  * Efficient training and inference methods;  * Motion and tracking for driver and passengers;  * Transfer learning;  * Video analysis and understanding of vehicle interior scenes;  * Computer vision + other modalities for vehicle interior analysis.
 Paper Submission ------------------ The workshop welcomes submissions of full papers, as well as short papers reporting new, unpublished, original research. Full papers should not exceed 14 pages and be formatted using ECCV guidelines. Short papers should not exceed 8 pages.
Papers must contain no information identifying the author(s) or their organization(s) and should be submitted electronically via the workshop�s CMT Website: https://www.easychair.org/conferences/?conf=ism2022
 Review Process ---------------- Papers will be reviewed in a double-blind procedure by the international program committee. Papers will be judged on their relevance, novelty, scientific contribution, technical content, and clarity of presentation.
Publication ----------- Accepted papers will be published in the conference proceedings.
 Workshop Chairs ------------------ Jaime S. Cardoso, University of Porto and INESC TEC, Portugal Pedro M. Carvalho, INESC TEC and Polytechnic of Porto, Portugal Joao R. Pinto, Bosch Car Multimedia and University of Porto, Portugal Paula Viana, Polytechnic of Porto and INESC TEC, Portugal Christer Ahlstrom, VTI, Sweden Carolina Pinto, Bosch Car Multimedia, Portugal

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Deadline Extension – CFP RO-MAN 2022 Workshop on Machine Learning for HRI: Bridging the Gap between Action and Perception

July 5th, 2022 Daniela Lopez de Luise

CALL FOR PAPERS

The full-day virtual workshop:

Machine Learning for HRI: Bridging the Gap between Action and Perception (ML-HRI)

In conjunction with the 31st IEEE International Conference on Robot and Human Interactive Communication (RO-MAN) – August 22, 2022   

Webpage: https://ml-hri2022.ivai.onl/

I. Aim and Scope

A key factor for the acceptance of robots as partners in complex and dynamic human-centered environments is their ability to continuously adapt their behavior. This includes learning the most appropriate behavior for each encountered situation based on its specific characteristics as perceived through the robots senors. To determine the correct actions the robot has to take into account prior experiences with the same agents, their current emotional and mental states, as well as their specific characteristics, e.g. personalities and preferences. Since every encountered situation is unique, the appropriate behavior cannot be hard-coded in advance but must be learned over time through interactions. Therefore, artificial agents need to be able to learn continuously what behaviors are most appropriate for certain situations and people based on feedback and observations received from the environment to enable more natural, enjoyful, and effective interactions between humans and robots.

This workshop aims to attract the latest research studies and expertise in human-robot interaction and machine learning at the intersection of rapidly growing communities, including social and cognitive robotics, machine learning, and artificial intelligence, to present novel approaches aiming at integrating and evaluating machine learning in HRI. Furthermore, it will provide a venue to discuss the limitations of the current approaches and future directions towards creating robots that utilize machine learning to improve their interaction with humans.

II. Keynote Speakers and Panelists

  1. Dorsa Sadigh – Stanford University – USA
  2. Oya Celiktutan – King's College London – UK
  3. Sean Andrist – Microsoft – USA
  4. Stefan Wermter – University of Hamburg – Germany

III. Submission

  1. For paper submission, use the following EasyChair web link: Paper Submission.
  2. Use the RO-MAN 2022 format: RO-MAN Papers Templates.
  3. Submitted papers should be 4-6 pages for regular papers and 2 pages for position papers.

    The primary list of topics covers the following points (but not limited to):

  • Autonomous robot behavior adaptation
  • Interactive learning approaches for HRI
  • Continual learning
  • Meta-learning
  • Transfer learning
  • Learning for multi-agent systems
  • User adaptation of interactive learning approaches
  • Architectures, frameworks, and tools for learning in HRI
  • Metrics and evaluation criteria for learning systems in HRI
  • Legal and ethical considerations for real-word deployment of learning approaches

IV. Important Dates

  1. Paper submission: June 17, 2022 July 15, 2022 (AoE)
  2. Notification of acceptance: August 1, 2022 August 7, 2022 (AoE)
  3. Camera ready: August 14, 2022 (AoE)
  4. Workshop: August 22, 2022

V. Organizers

  1. Oliver Roesler – IVAI – Germany
  2. Elahe Bagheri – IVAI – Germany
  3. Amir Aly – University of Plymouth – UK
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CFP – IJCB 2022 – Special Session – Recent Advances in Detecting Manipulation Attacks on Biometric Systems (ADMA-2022)

July 5th, 2022 Daniela Lopez de Luise
Recent Advances in Detecting Manipulation Attacks on Biometric Systems (ADMA-2022)

IJCB 2022 – Special Session

 

Website: https://sites.google.com/view/ijcb-ss-adma-2022/home

 

 

Manipulated attacks in biometrics via modified images/videos and other material-based techniques such as presentation attacks and deep fakes have become a tremendous threat to the security world owing to increasingly realistic spoofing methods. Hence, such manipulations have triggered the need for research attention towards robust and reliable methods for detecting biometric manipulation attacks. The recent inclusion of manipulation/generation methods such as auto-encoder and generative adversarial network approaches combined with accurate localisation and perceptual learning objectives added an extra challenge to such manipulation detection tasks. Due to this, the performance of existing state-of-the-art manipulation detection methods significantly degrades in the unknown scenarios. Apart from this, real-time processing, manipulation on low-quality medium, limited availability of data, and inclusion of these manipulation detection techniques for forensic investigation are yet to be widely explored. Hence, this special session aims to profile recent developments and push the border of the digital manipulation detection technique on biometric systems.

 

We invite practitioners, researchers and engineers from biometrics, signal processing, material science, mathematics, computer vision and machine learning to contribute their expertise to underpin the highlighted challenges. Further, this special session promote cross disciplinary research by inviting the partitioner in the field of psychology where one can perform the human observer (or super-recogniser) analysis to detect attacks.

 

Topics of interest include but not limited to:

             Deepfake manipulation and detection technique

             Novel generalised PAD to unknown attacks

             Image manipulation techniques datasets

             Database in image and video manipulation, and attacks

             Privacy preserving techniques in digital manipulation attack detection

             Image and video synthesis in PAD

             Image and video manipulation generation and detection

             Human observer analysis in detecting the manipulated biometric images

             Novel sensors for detecting manipulated attacks

             Bias analyses and mitigation in attack detection algorithms

 

Submission Guidelines:

             Submit your papers at: https://cmt3.research.microsoft.com/IJCB2022 in a special session track.

             Paper presented at ADMA-2022 will be published as part of the IJCB2022 and should, therefore, follow the same guideline as the main conference.

             Page limit: A paper can be up to 8 pages including figures and tables, plus additional pages for references only. If the 7th and 8th pages contain any content other than references, they will incur a cost of USD100 per page.

             Papers will be double-blind peer reviewed by at least three reviewers. Please remove author names, affiliations, email addresses, etc. from the paper. Remove personal acknowledgments.

 

Important Dates:

             Full Paper Submission: July 27, 2022, 23:59:59 PDT

             Acceptance Notice: August 17, 2022, 23:59:59 PDT

             Camera-Ready Paper: August 24, 2022, 23:59:59 PDT

 

Organizing Committee:

Assoc. Prof. Abhijit Das, BITS Pilani, India

Prof. Raghavendra Ramachandra, NTNU, Norway

Meiling Fang, Fraunhofer IGD, Germany

Dr. Naser Damer, Fraunhofer IGD, Germany

Prof. Hu Han, CAS, China

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DeepLearn 2023 Winter: early registration July 4

July 5th, 2022 Daniela Lopez de Luise
******************************************************************
 
 
 
8th INTERNATIONAL SCHOOL ON DEEP LEARNING
 
 
 
DeepLearn 2023 Winter
 
 
 
Bournemouth, UK
 
 
 
January 16-20, 2023
 
 
 
 
 
 
***********
 
 
 
Co-organized by:
 
 
 
Department of Computing and Informatics
 
Bournemouth University
 
 
 
Institute for Research Development, Training and Advice – IRDTA
 
Brussels/London
 
 
 
******************************************************************
 
 
 
Early registration: July 4, 2022
 
 
 
******************************************************************
 
 
 
SCOPE:
 
 
 
DeepLearn 2022 Winter 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 and Luleå.
 
 
 
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, 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 2023 Winter 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 Winter will take place in Bournemouth, a coastal resort town on the south coast of England. The venue will be:
 
 
 
Talbot Campus
 
Bournemouth University
 
 
 
 
 
 
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:
 
 
 
Yi Ma (University of California, Berkeley), CTRL: Closed-Loop Data Transcription via Rate Reduction
 
 
 
Daphna Weinshall (Hebrew University of Jerusalem), Curriculum Learning in Deep Networks
 
 
 
Eric P. Xing (Carnegie Mellon University), It Is Time for Deep Learning to Understand Its Expense Bills
 
 
 
PROFESSORS AND COURSES: (to be completed)
 
 
 
Mohammed Bennamoun (University of Western Australia), [intermediate/advanced] Deep Learning for 3D Vision
 
 
 
Matias Carrasco Kind (University of Illinois, Urbana-Champaign), [intermediate] Anomaly Detection
 
 
 
Nitesh Chawla (University of Notre Dame), [introductory/intermediate] Graph Representation Learning
 
 
 
Seungjin Choi (Intellicode), [introductory/intermediate] Bayesian Optimization over Continuous, Discrete, or Hybrid Spaces
 
 
 
Sumit Chopra (New York University), [intermediate] Deep Learning in Healthcare
 
 
 
Luc De Raedt (KU Leuven), [introductory/intermediate] Statistical Relational and Neurosymbolic AI
 
 
 
Marco Duarte (University of Massachusetts, Amherst), [introductory/intermediate] Explainable Machine Learning
 
 
 
João Gama (University of Porto), [introductory] Learning from Data Streams: Challenges, Issues, and Opportunities
 
 
 
Claus Horn (Zurich University of Applied Sciences), [intermediate] Deep Learning for Biotechnology
 
 
 
Zhiting Hu (University of California, San Diego) & Eric P. Xing (Carnegie Mellon University), [intermediate/advanced] A “Standard Model” for Machine Learning with All Experiences
 
 
 
Nathalie Japkowicz (American University), [intermediate/advanced] Learning from Class Imbalances
 
 
 
Gregor Kasieczka (University of Hamburg), [introductory/intermediate] Deep Learning Fundamental Physics: Rare Signals, Unsupervised Anomaly Detection, and Generative Models
 
 
 
Karen Livescu (Toyota Technological Institute at Chicago), [intermediate/advanced] Speech Processing: Automatic Speech Recognition and beyond (to be confirmed)
 
 
 
David McAllester (Toyota Technological Institute at Chicago), [intermediate/advanced] Information Theory for Deep Learning
 
 
 
Dhabaleswar K. Panda (Ohio State University), [intermediate] Exploiting High-performance Computing for Deep Learning: Why and How?
 
 
 
Fabio Roli (University of Cagliari), [introductory/intermediate] Adversarial Machine Learning
 
 
 
Richa Singh (Indian Institute of Technology Jodhpur), [introductory/intermediate] Trusted AI
 
 
 
Kunal Talwar (Apple), [introductory/intermediate] Foundations of Differentially Private Learning
 
 
 
Tinne Tuytelaars (KU Leuven), [introductory/intermediate] Continual Learning in Deep Neural Networks
 
 
 
Lyle Ungar (University of Pennsylvania), [intermediate] Natural Language Processing using Deep Learning
 
 
 
Bram van Ginneken (Radboud University Medical Center), [introductory/intermediate] Deep Learning for Medical Image Analysis
 
 
 
Yu-Dong Zhang (University of Leicester), [introductory/intermediate] Convolutional Neural Networks and Their Applications to COVID-19 Diagnosis
 
 
 
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 January 8, 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 January 8, 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 January 8, 2023.
 
 
 
ORGANIZING COMMITTEE:
 
 
 
Rashid Bakirov (Bournemouth, local co-chair)
 
Marcin Budka (Bournemouth)
 
Vegard Engen (Bournemouth)
 
Nan Jiang (Bournemouth, local co-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 are available 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:
 
 
 
Bournemouth University
 
 
 
Rovira i Virgili University
 
 
 
Institute for Research Development, Training and Advice – IRDTA, Brussels/London
      
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