Towards Multimodal Foundational Models for Modelling Visual Cortex Call for Papers

--- Call for papers - Apologies for multiple posting --- 

Towards Multimodal Foundational Models for Modelling Visual Cortex 

Held in conjunction with the 
8th European Conference on Computer Vision ECCV 2024 -
Sep 29th - Oct 4th, 2024, 2024, Milan (Italy)  

Web site: https://fmbrain-eccv.github.io
 

IMPORTANT DATES 

Paper submission deadline: July 15, 2024 

Notification to authors: August 10, 2024 

Camera-ready deadline: August 20, 2024 

 

CALL FOR PAPERS  

Recent advancements in artificial intelligence and computational
 modeling have sparked a new era in understanding complex systems,
 particularly in neuroscience. Foundation models, distinguished by
 their capability to integrate diverse data modalities, have emerged
 as powerful tools in decoding the complexities of the visual cortex
 in both human and animal subjects. This workshop aims to explore
 state-of-the-art techniques and methodologies for developing
 multimodal foundational models that comprehensively represent the
 visual cortex.

Topics (but not limited to): 


    Theoretical Frameworks and Computational Approaches: Novel
    theoretical constructs and computational strategies for modeling
    the visual cortex using multimodal data.

    Integration of Diverse Data Sources: Techniques and challenges in
    integrating and harmonizing heterogeneous data modalities such as
    fMRI, EEG, in vivo two-photon calcium imaging, fNIRS, and others.

    Learning Paradigms for Noisy Data: Innovations in learning
    algorithms and paradigms to effectively handle noisy and
    incomplete data in modeling brain functions.

    Applications in Neuroscientific Research: Practical applications
    of multimodal foundational models in elucidating perception,
    cognition, and neurodevelopmental or neurodegenerative disorders.

    Contrastive Learning for Multimodal Brain Data Fusion: Techniques
    and advancements in leveraging contrastive learning methods to
    fuse multimodal brain data for enhanced representation and
    analysis.

    Self-Supervised Learning for Temporal Brain Dynamics: Approaches
    utilizing self-supervised learning to capture and model temporal
    dynamics in brain imaging and physiological data.

    Unsupervised Learning for Structural and Functional Brain Network
    Construction: Methods employing unsupervised learning to construct
    and analyze structural and functional brain networks from
    multimodal data.

    Weakly Supervised Learning for Brain Connectivity Analysis:
    Innovations in weakly supervised learning techniques for analyzing
    brain connectivity patterns and networks.

    Foundational Models for Classification and Predictive Modeling:
    Development and application of foundational models for
    classification and predictive modeling tasks in neuroscience.

    Multimodal Brain Image Visualization with Advanced Learning
    Techniques: Techniques for visualizing multimodal brain images
    using advanced learning and visualization methods to aid in data
    interpretation.

    Ethical Implications: Ethical considerations in the creation, use,
    and implications of foundational models in neuroscience research
    and applications.


SUBMISSION GUIDELINES: 

We invite original contributions in the form of full research papers,
 review articles, or extended abstracts of ongoing works aligned with
 the workshop's themes. Full papers should not exceed 8 pages
 (excluding references), while extended abstracts should be limited to
 4 pages. All submissions will undergo a rigorous peer-review process
 conducted by experts in the field.

Submission follows the ECCV format (see the ECCV website for format
instruction
(https://eccv.ecva.net/Conferences/2024/AuthorGuide)).
  

WORKSHOP PROCEEDINGS 

All accepted contributed papers will appear in the ECCV2024 workshop proceedings. 

 

WORKSHOP ORGANIZERS  

Nouria Lakhdar Ghazal - Mohammed V University of Rabat, Morocco  

Simone Palazzo – University of Catania, Italy 

Matteo Pennisi - Campus Bio Medico Rome, Italy 

Federica Proietto Salanitri - University of Catania, Italy 

Shanmuganathan Raman - Indian Institute of Technology, India 

Concetto Spampinato - University of Catania, Italy 

Andreas Tolias - University of Houston, Texas 

Jonathan Xu - University of Waterloo, Canada