Special Issue on Towards Responsible and Explainable Multi-Modal Fusion Call for Papers

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*** Call for Papers - 
ACM Transactions on Multimedia Computing, Communications, and Applications ***

Special Issue on Towards Responsible and Explainable Multi-Modal Fusion

PAPER SUBMISSION DEADLINE: August 31, 2026

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OVERVIEW

Multi-modal fusion, defined as the integration of heterogeneous data
sources such as text, images, audio, sensor streams, and structured
records, has become a cornerstone of modern artificial intelligence
with applications across healthcare, autonomous systems, social
computing, and multimedia analysis. By combining complementary
information from different modalities, fusion techniques enhance
contextual understanding, robustness, and predictive performance. As
these systems grow in complexity and are increasingly adopted in
high-stakes scenarios, it becomes essential to embed responsibility,
transparency, and fairness as intrinsic requirements in both design
and evaluation.
 
This Special Issue aims to consolidate emerging contributions that
embed explainability, interpretability, and ethical principles
directly into the architecture, training, and evaluation of
multi-modal systems. We particularly encourage submissions addressing
high-impact domains such as healthcare, autonomous systems, and social
computing, where trustworthy AI is critical.

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SPECIAL ISSUE TOPICS

Topics include but are not limited to:

     Responsible and explainable multi-modal fusion architectures and evaluation protocols 
    Integration and alignment of foundation models and multi-modal large language models for interpretable decision-making. 
    Generative and synthetic data for responsible and robust multi-modal fusion. 
    Bias detection and mitigation, fairness assessment, and accountability in multi-modal systems. 
    Transfer learning and domain adaptation for adaptive and trustworthy multi-modal fusion. 
    Adversarial robustness and security in cross-modal environments. 
    Continual and lifelong learning in evolving multi-modal settings. 
    Federated, edge, and privacy-preserving multi-modal learning frameworks.  

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IMPORTANT DATES

    Submission deadline: August 31, 2026
    First-round review decisions: October 31, 2026
    Deadline for revised submissions: December 15, 2026
    Notification of final decisions: February 15, 2027
    Tentative publication: April 2027

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GUEST EDITORS

    Lucia Cascone - University of Salerno, Italy (lcascone@unisa.it)
    Emanuela Marasco - Virginia Commonwealth University, USA (marascoe@vcu.edu)
    Imad Rida - Université de Technologie de Compiègne, France (imad.rida@utc.fr)

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SUBMISSION AND REVIEW DETAILS

Prospective authors are invited to submit their manuscripts
electronically in accordance with the ACM TOMM author guidelines
(https://tomm.acm.org/authors.cfm). Manuscripts that do not comply
with these guidelines will not be considered. Submissions must fall
within the scope of ACM TOMM.

Please submit your papers through the online system
(https://mc.manuscriptcentral.com/tomm) and ensure that you select the
appropriate Special Issue during submission. Manuscripts must not be
previously published or under review elsewhere.

For questions or further information, please contact:
Lucia Cascone (lcascone@unisa.it)