WACV2022 :: xAI4Biometrics Workshop :: CfP :: EXTENDED DEADLINE 25/10
October 12th, 2021 
Daniela Lopez de Luise xAI4Biometrics Workshop @ WACV 2022 :: Call for Papers
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- Walter J. Scheirer, Notre Dame University, USA
- Speaker TBA
Abstract submission:
Full Paper Submission Deadline:
Acceptance Notification: November 15, 2021
Camera-ready & Registration: November 19, 2021
Conference: January 04-08, 2022 | Workshop Date: January 04, 2022
The xAI4Biometrics welcomes works that focus on biometrics and promote the development of:
- Methods to interpret the biometric models to validate their decisions as well as to improve the models and to detect possible vulnerabilities;
- Quantitative methods to objectively assess and compare different explanations of the automatic decisions;
- Methods and metrics to study/evaluate the quality of explanations obtained by post-model approaches and improve the explanations;
- Methods to generate model-agnostic explanations;
- Transparency and fairness in AI algorithms avoiding bias;
- Methods that use post-model explanations to improve the models’ training;
- Methods to achieve/design inherently interpretable algorithms (rule-based, case-based reasoning, regularization methods);
- Study on causal learning, causal discovery, causal reasoning, causal explanations, and causal inference;
- Natural Language generation for explanatory models;
- Methods for adversarial attacks detection, explanation and defense (“How can we interpret adversarial examples?”);
- Theoretical approaches of explainability (“What makes a good explanation?”);
- Applications of all the above including proofs-of-concept and demonstrators of how to integrate explainable AI into real-world workflows and industrial processes.
ORGANIZING COMMITTEES
GENERAL CHAIRS
- Jaime S. Cardoso, INESC TEC and University of Porto, Portugal
- Ana F. Sequeira, INESC TEC, Porto, Portugal
- Arun Ross, Michigan State University, USA
- Peter Eisert, Humboldt University & Fraunhofer HHI
- Cynthia Rudin, Duke University, USA
PROGRAMME CHAIRS
- Christoph Busch, NTNU & Hochschule Darmstadt
- Tiago de Freitas Pereira, IDIAP Research Institute, Switzerland
- Wilson Silva, INESC TEC and University of Porto, Portugal
Ana Filipa Sequeira, PhD (ana.f.sequeira@inesctec.pt)
Assistant Researcher
INESC TEC, Porto, Portugal
International Workshop on Face and Gesture Analysis for COVID-19 (@FG2021)
October 12th, 2021 Daniela Lopez de Luise Held in conjunction with FG 2021 (http://iab-rubric.org/fg2021/)
https://fg4covid19.github.io/#
Paper submission deadline (round 2) extedned to: October 17th, 2021
11:59 PST
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ORGANIZERS
Hazım Kemal Ekenel, Istanbul Technical University, Turkey
Fevziye Irem Eyiokur, Karlsruhe Institute of Technology, Germany
Muhammad Imran, Qatar Computing Research Institute, Qatar
Alperen Kantarcı, Istanbul Technical University, Turkey
Ferda Ofli, Qatar Computing Research Institute, Qatar
Vitomir Štruc, University of Ljubljana, Slovenia
Alexander Waibel, Karlsruhe Institute of Technology, Germany and
Carnegie Mellon University, Pittsburgh
CO-ORGANIZER
European Association for Biometrics (EAB)
ABOUT
Since the end of 2019, the whole world has been living with the effects
of the COVID-19 disease. Due to the airborne nature of the novel
coronavirus, the COVID-19 disease has spread rapidly in many countries
in a short time and has also been declared a global pandemic. Several
preventive measures were implemented to reduce the spread of this
disease, including social distancing, wearing face masks, avoiding large
crowds, and others.
To help monitor public behavior and compliance with the implemented
prevention measures, to gather reliable data for epidemiological models,
facilitate authorized access and authentication, ensure safety and
security despite the COVID-19 pandemic, it is critical to develop
automated machine learning and computer vision based techniques that can
help with these tasks. The COVID-19 pandemic has initiated research and
development of computer-aided systems that can be used to prevent and
monitor not only COVID-19 but also similar diseases that may appear in
the future.
The goal of this workshop is to present the most recent and advanced
work related to face and gesture analysis in the scope of COVID-19. We
are interested in all COVID-19 related studies that focus on the
detection of face mask, subjects' behaviors in public areas, analysis of
pandemic and preventions-related real-world face and gesture data,
prevention control and monitoring systems for unconstrained real-world
conditions and related problems.
TOPICS OF INTEREST
Topics of interest include, but are not limited to:
+ Face mask detection,
+ Masked face detection and recognition,
+ Face-hand interaction detection,
+ COVID-19 monitoring and control systems related to face and gesture
analysis,
+ Social distance tracking and control systems,
+ Dataset bias analysis on COVID-19 datasets (face mask, face-hand,
social distance,…),
+ Face, gesture and body analysis for monitoring intervention-measure
compliance,
+ Behavior understanding relevant for COVID-19,
+ Analysis of social interactions relevant to COVID-19,
+ Soft-biometric analysis on masked faces (age, gender, …),
+ Generative models relevant in the scope of COVID-19,
+ Novel datasets and performance benchmarks,
+ Related applications.
PAPER SUBMISSION
For submission instructions visit: https://fg4covid19.github.io/index.html
Accepted papers will be published in the “Proceedings of FG 2021” and
included in IEEE Xplore.
Selected papers from the first submission round will be invited to
submit extended versions of their work to a special issue organized in
the Image and Vision Computing journal.
KEYNOTE SPEAKERS
Naser Damer, Fraunhofer IGD, Germany
Albert Ali Salah, Utrecht University, The Netherlands
Please feel free to reach out for further details.
Hazım Kemal Ekenel, Fevziye Irem Eyiokur, Muhammad Imran, Alperen
Kantarcı, Ferda Ofli, Vitomir Štruc, Alexander Waibel
FG4COVID19 2021 Organizers
Special Issue on Ambient Understanding for Mobile Autonomous Robots (AutoRob)
October 12th, 2021 Daniela Lopez de Luise Special Issue on Ambient Understanding for Mobile Autonomous Robots
Journal of Ambient Intelligence and Humanized Computing (AIHC)
Journal Impact Factor (2020): 7.104 (ISSN: 1868-5137)
Website: https://www.springer.com/journal/12652/updates/19709550
Submission Deadline: 28th February 2022
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=== Call for papers ===
In recent years, the combination of robotics and artificial intelligence arose outstanding developments in the fields of cognitive robotics and human robot interaction. Nowadays several academic and industrial research groups are engaged in the design of intelligent robots able to act autonomously, by means of advanced deep learning algorithms used for the analysis of data acquired from heterogeneous sensors (camera, 3D camera, stereo camera, microphone, LIDAR, sonar etc.), for ambient understanding (people, objects, scenes) and for dynamically adapting the interaction with humans and environment.
Despite the recent deep learning achievements and the impressive advances in technology for intelligent robot systems, there are still several challenges to be addressed in computer vision, audio analysis, sensor fusion, autonomous navigation methods and human robot interaction. Such challenges arise from the inherent difficulty of designing algorithms that are effective in the complex real world, but are also due to the acquisition of low quality or corrupted images, noisy audio and inaccurate data from environmental sensors, and to the necessity to process large amounts of operations in real-time.
The aim of the special issue is to provide a collection of innovative algorithms, theories and applications related to all the aspects of perception, reasoning and navigation of a mobile autonomous robot, with contributions from both academia and industry. Even the submission of datasets and benchmarks collected in real challenging conditions or methods optimized for real-time perception and reasoning are encouraged.
Topics of interest are, but not limited to:
• Face Analysis (Detection, Recognition, Re-Identification, Gender, Age, Ethnicity, Emotion)
• People detection
• 2D and 3D object detection and recognition for ambient understanding and reconstruction
• 6D Pose Estimation
• Gesture and action recognition
• Voice and audio event detection and localization
• Speech Analysis for human-robot interaction (Speaker Recognition, Gender, Age, Language, Emotion, Speech to text, Natural Language Processing)
• Multi-modal perception for human-robot interaction and autonomous navigation
• Ambient understanding for SLAM, dynamic motion planning and autonomous navigation
• Reinforcement learning for cognitive robotics
• Datasets and benchmarks in the wild
• Software optimization for real-time perception, reasoning and navigation
• Embedded systems for cognitive robotics
• Algorithms for autonomous car driving
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=== Guest editors ===
Antonio Greco (Università degli Studi di Salerno, Italy)
Mario Vento (Università degli Studi di Salerno, Italy)
Sean Ryan Fanello (Google, USA)
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Antonio Greco, Ph. D.
Assistant Professor
Dept. of Computer Engineering, Electrical Engineering and Applied Mathematics (DIEM)
Laboratory of Intelligent Machines for the Analysis of Videos, Images and Audio (MIVIA)
University of Salerno, Via Giovanni Paolo II, 132 84084 – Fisciano (SA) Italy
Phone:+39 089 963006
Web:
http://docenti.unisa.it/antonio.greco
http://mivia.unisa.it/people/greco/
Skype: antonio.greco17589
Learning High-Speed Robot Navigation in the Wild, from images to control commands
October 12th, 2021 Daniela Lopez de Luise High-Speed Flight in the Wild”.
The code allows you to train end-to-end navigation policies to navigate
drones (or any other mobile robots) at very high speeds in previously
unknown, challenging environments (snowy terrains, derailed trains,
ruins, thick vegetation, and collapsed buildings), with only onboard
sensing and computation. Training is done exclusively in simulation with
imitation learning from a privileged expert. Thanks to a sensor
abstraction procedure, the policies trained in simulation can be applied
to a real platform without any fine-tuning on real data!
All the code and datasets to reproduce the experiments you see in the
video are released for free to the public. Paper, code, dataset, video:
http://rpg.ifi.uzh.ch/AgileAutonomy.html
Antonio Loquercio, Elia Kaufmann, Rene Ranflt, Matthias Mueller, Vladlen
Koltun, Davide Scaramuzza