Toward Trustworthy Vision-language Models in the Wild: Theory, Algorithm and Application

Peng, B; Du, X; Fang, Z

Toward Trustworthy Vision-language Models in the Wild: Theory, Algorithm and Application

Peng, B Du, X Fang, Z

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Publisher:: Association for Computing Machinery (ACM)
Publication Type:: Conference Proceeding
Citation:: Icmr 2026 Proceedings of the 16th ACM International Conference on Multimedia Retrieval, 2026, pp. 2906-2907
Issue Date:: 2026-06-15

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Field	Value	Language
dc.contributor.author	Peng, B
dc.contributor.author	Du, X
dc.contributor.author	Fang, Z https://orcid.org/0000-0003-0602-6255
dc.date.accessioned	2026-10-09T07:50:34Z
dc.date.available	2026-10-09T07:50:34Z
dc.date.issued	2026-06-15
dc.identifier.citation	Icmr 2026 Proceedings of the 16th ACM International Conference on Multimedia Retrieval, 2026, pp. 2906-2907
dc.identifier.uri	http://hdl.handle.net/10453/197170
dc.description.abstract	Vision-Language Models (VLMs) have revolutionized multimedia applications by enabling open-vocabulary search and generation. While significant strides have been made in developing powerful VLMs, the field remains in the early stages of rigorously evaluating and understanding their real-world vulnerabilities, both empirically and theoretically. In response to this important but under-explored gap, the workshop aims to provide a forum for researchers to share advanced in theories, algorithms and applications of trustworthy multi-modal learning, fostering diverse viewpoints on core principles and emerging techniques for developing trustworthy VLMs in the wild. The workshop provides a platform for researchers to showcase their work, exchange ideas, and foster potential collaborations. Additionally, it serves as a valuable opportunity for practitioners to stay abreast of the latest developments in developing trustworthy vision-language models.
dc.language	en
dc.publisher	Association for Computing Machinery (ACM)
dc.relation.ispartof	Icmr 2026 Proceedings of the 16th ACM International Conference on Multimedia Retrieval
dc.relation.ispartof	Proceedings of the 2026 International Conference on Multimedia Retrieval
dc.relation.isbasedon	10.1145/3805622.3811235
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Toward Trustworthy Vision-language Models in the Wild: Theory, Algorithm and Application
dc.type	Conference Proceeding
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Australian Artificial Intelligence Institute (AAII)
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science/Australian Artificial Intelligence Institute
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2026-10-09T07:50:33Z
pubs.publication-status	Published

Abstract:

Vision-Language Models (VLMs) have revolutionized multimedia applications by enabling open-vocabulary search and generation. While significant strides have been made in developing powerful VLMs, the field remains in the early stages of rigorously evaluating and understanding their real-world vulnerabilities, both empirically and theoretically. In response to this important but under-explored gap, the workshop aims to provide a forum for researchers to share advanced in theories, algorithms and applications of trustworthy multi-modal learning, fostering diverse viewpoints on core principles and emerging techniques for developing trustworthy VLMs in the wild. The workshop provides a platform for researchers to showcase their work, exchange ideas, and foster potential collaborations. Additionally, it serves as a valuable opportunity for practitioners to stay abreast of the latest developments in developing trustworthy vision-language models.

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http://hdl.handle.net/10453/197170