BIOVID Challenge and Workshop at ICIAP 2025

April 30th, 2025 Daniela Lopez de Luise
Call for Papers & Participation – Biometric Authentication Challenge @ ICIAP 2025
Dear colleagues,
We are excited to invite you to participate in the Dual Factor Lip-Based Biometric User Authentication Workshop and Challenge, which will be held in conjunction with ICIAP 2025.
This event focuses on cutting-edge approaches to biometric authentication using a unique dataset of lip movement videos. Participants will develop systems that combine visual and audio cues—specifically, lip dynamics and spoken passphrases—to verify user identity under open-set conditions. The challenge pushes the boundaries of multimodal biometrics, encouraging research in feature extraction, cross-modal learning, privacy-preserving AI, and robustness across devices and conditions.
The training set will be released on April 30, 2025, and the test set—available after abstract submission—will be released by May 20, 2025, at the latest. The final deadline to submit a paper to the workshop is July 10, 2025.
All accepted workshop papers will be published in a dedicated volume of the Springer Lecture Notes in Computer Science (LNCS) as part of the ICIAP 2025 proceedings.
Top-performing teams will be invited to present their work at the workshop, and selected contributions will be invited to submit an extended version of their paper to the MDPI Applied Sciences Special Issue on “Recent Advances in Biometrics and Multimodal User Authentication”: https://www.mdpi.com/journal/applsci/special_issues/XV7PJA0Y89
Submissions will be evaluated based on Equal Error Rate (EER) and authentication accuracy. Whether you are working on deep learning, computer vision, speech processing, or privacy-aware AI, this is an excellent opportunity to benchmark your solutions and contribute to the development of secure, interpretable biometric systems.
To register your team for the competition, access the dataset, and learn more, please visit: https://www.dmi.unict.it/spata/BiovidChallenge/
Please don’t hesitate to contact us at biometric.dataset.25@gmail.com for any further information.
We look forward to your participation!
Best regards,  
The Organizing Committee  
Massimo Orazio Spata, Georgia Fargetta, Alessandro Ortis  
University of Catania

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LAVA’25: Call for Papers and Challenge Participation

April 30th, 2025 Daniela Lopez de Luise
Call for Papers and Challenge Participation

We invite researchers, practitioners, and enthusiasts to contribute to the Workshop and Grand Challenge on Large Vision–Language Model Learning and Applications (LAVA), to be held in conjunction with ACM Multimedia 2025.

 

🔬 LAVA Workshop Overview

The LAVA Workshop explores innovations and challenges in Large Vision–Language Models (LVLMs). We welcome contributions across a broad spectrum of topics, including but not limited to:

  • Data preprocessing and prompt engineering for LVLMs
  • Training and compression techniques for LVLMs
  • Self-supervised, unsupervised, few-shot, and zero-shot learning
  • Generative AI and multimodal generation
  • Trustworthy and explainable LVLMs
  • Security, privacy, and ethical concerns in LVLMs
  • Evaluation and benchmarking methodologies
  • LVLMs for downstream tasks and applications
  • LVLMs in virtual, augmented, and mixed reality
  • LVLMs for low-resource scenarios
  • Multimodal integration beyond vision and language

Submission Types

  • Short papers (non-archived): Up to 4 pages, excluding references
  • Long papers (archived in ACM Digital Library): Up to 8 pages, excluding references

All submissions should follow the official ACM MM format.

Workshop Important Dates

  • 📄 Paper submission deadline: June 15, 2025
  • 🚀 ACM MM fast-track submission: July 11, 2025
  • ✅ Notification of acceptance: July 24, 2025
  • 🖋️ Camera-ready deadline: August 1, 2025
  • 📅 Workshop date: October 27–28, 2025

🔗 More info: https://lava-workshop.github.io/workshop

 

🏆 LAVA Grand Challenge 2025

This year's LAVA Challenge focuses on enhancing LVLM capabilities in interpreting complex visual documents, including: Data Flow Diagrams (DFDs), Class Diagrams, Gantt Charts, Architectural and Building Design Drawings

The 2025 challenge emphasizes Japanese government and business documents in PDF format, each accompanied by multiple-choice (10-option) questions requiring deep visual–linguistic understanding.

Challenge Important Dates

  • ✅ Registration opens: March 15, 2025
  • 📂 Public data release: April 17, 2025
  • ⏳ Registration closes: May 31, 2025
  • 🔐 Private test data release: We decided to use the test data for public and private leaderboard.
  • 📝 Final results, report & paper submission deadline: June 30, 2025
  • 📢 Notification of acceptance: July 24, 2025
  • 🖋️ Camera-ready deadline: August 26, 2025
  • 📅 Challenge presentation date: October 27–31, 2025

🔗 More info: https://lava-workshop.github.io/grandchallenge

 

We look forward to your contributions and participation in pushing the frontiers of vision–language learning!

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CfP: ACM Multimedia 2025 Grand Challenge “MultiMediate”

April 30th, 2025 Daniela Lopez de Luise
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Dr. Philipp Müller 
Senior Researcher

Deutsches Forschungszentrum für Künstliche Intelligenz GmbH (DFKI)
Stuhlsatzenhausweg 3, Campus D3 1
66119 Saarbrücken
Germany
+49 681 85775 7752
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Deutsches Forschungszentrum für Künstliche Intelligenz GmbH
Firmensitz: Trippstadter Straße 122, D-67663 Kaiserslautern 
Geschäftsführung: Prof. Dr. Antonio Krüger (Vorsitzender)
Helmut Ditzer
Vorsitzender des Aufsichtsrats: 
Dr. Ferri Abolhassan 
Amtsgericht Kaiserslautern, HRB 2313 
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1st CFP due July 15: CAIS 2025 Automated and Intelligent Systems, Oct 1-2, Online & OKCity, USA

April 30th, 2025 Daniela Lopez de Luise

FairBench: a Python library for comprehensive AI fairness exploration

April 30th, 2025 Daniela Lopez de Luise
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we would like to announce the release of FairBench, a comprehensive Python library for exploring and understanding AI biases and fairness.

Why an(other) AI fairness library?

As AI systems become ingrained in everyday lives, it is important to ensure their fairness across different demographic groups — or group intersections. 
But it can be challenging to navigate through the many algorithmic bias/fairness definitions and metrics in a standardized way.
 
Introducing FairBench: a partner in fair AI development
FairBench provides a robust and flexible platform for in-depth AI fairness exploration, for example, as part of your AI fairness compliance plan.
It composes fairness definitions from a growing list of simpler building blocks, and lets you automatically or manually combine and run those in a couple lines of code.

Take advantage of features designed to help grasp a broad picture of systems:

🧱 Measures are built from simpler blocks through a standard scheme that makes it easier to understand what each one represents.
📈 Generate fairness reports and stamps for classification, recommendation, ranking, or scoring tasks. Reports contain descriptions of their values, can be saved, and can be compared to track progress as datasets evolve or between different algorithm versions.
⚖️  Perform analysis across multiple multi-value sensitive attributes and their intersections. Visualize the results in the console or in the browser, or export them in various formats (HTML text, json, etc) for integration in your pipelines.
🧪 Filter reports (simplify/transform/extract ad-hoc summaries) to get insights about where to start your investigation, and backtrack to the intermediate computations of worrisome values to get a feel for algorithmic issues at play.
🖥️   ML compatible: can handle lists, arrays, dataframes, and tensors from popular frameworks. These could come from any modality you are working in (tabular data, images, graphs, text, etc.) 
📦 Comes together with exploratory datasets and algorithms, currently from the tabular and vision data modalities, for out-of-the-box experimentation.
 
We invite you to explore FairBench
Documentation with the ability to try it in your browser: https://fairbench.readthedocs.io/
If you are interested in more direct discussion or talking about the topic of AI fairness, join us in our Discord server: https://discord.gg/WwQWFSjSWZ
We are eager to hear your feedback and receive feature requests and bug reports via Discord, GitHub issues, or email. We welcome pull requests, and encourage you to join our community in advancing the field of AI fairness. 

Ready to assess AI fairness?
 
Sincerely,
Emmanouil Krasanakis

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