TY - GEN
T1 - McGE '25
T2 - 33rd ACM International Conference on Multimedia, MM 2025
AU - Jin, Cheng
AU - Song, Mingli
AU - Wang, Rui
AU - Wu, Xingjiao
N1 - Publisher Copyright:
© 2025 Owner/Author.
PY - 2025/10/27
Y1 - 2025/10/27
N2 - This workshop addresses next-generation methods in multimedia research, with a focus on content generation, quality assessment, and dataset development. These three areas are foundational for advancing multimedia technologies and applications. Emerging approaches in multimedia content generation, powered by generative AI and multimodal learning, are reshaping domains such as entertainment, advertising, education, and healthcare. At the same time, robust quality assessment is essential to ensure that generated content achieves high standards of perceptual fidelity, semantic consistency, and user satisfaction, thereby determining the real-world impact of multimedia systems. Datasets remain indispensable for training and evaluating algorithms, and innovative strategies in dataset construction-ranging from augmentation and annotation to addressing issues of bias and small-sample imbalance-are driving the development of more reliable and ethical multimedia applications. By convening leading researchers and practitioners, this workshop provides a platform to explore state-of-the-art methods, share best practices, and discuss open challenges in next-generation multimedia research. The goal is to foster interdisciplinary collaboration and inspire innovative solutions that advance the creation, evaluation, and application of multimedia content, setting new benchmarks for the field and shaping the future of multimedia technologies.
AB - This workshop addresses next-generation methods in multimedia research, with a focus on content generation, quality assessment, and dataset development. These three areas are foundational for advancing multimedia technologies and applications. Emerging approaches in multimedia content generation, powered by generative AI and multimodal learning, are reshaping domains such as entertainment, advertising, education, and healthcare. At the same time, robust quality assessment is essential to ensure that generated content achieves high standards of perceptual fidelity, semantic consistency, and user satisfaction, thereby determining the real-world impact of multimedia systems. Datasets remain indispensable for training and evaluating algorithms, and innovative strategies in dataset construction-ranging from augmentation and annotation to addressing issues of bias and small-sample imbalance-are driving the development of more reliable and ethical multimedia applications. By convening leading researchers and practitioners, this workshop provides a platform to explore state-of-the-art methods, share best practices, and discuss open challenges in next-generation multimedia research. The goal is to foster interdisciplinary collaboration and inspire innovative solutions that advance the creation, evaluation, and application of multimedia content, setting new benchmarks for the field and shaping the future of multimedia technologies.
KW - image synthesis
KW - multimedia content generation
KW - quality assessment
UR - https://www.scopus.com/pages/publications/105024069490
U2 - 10.1145/3746027.3762383
DO - 10.1145/3746027.3762383
M3 - 会议稿件
AN - SCOPUS:105024069490
T3 - MM 2025 - Proceedings of the 33rd ACM International Conference on Multimedia, Co-Located with MM 2025
SP - 14329
EP - 14330
BT - MM 2025 - Proceedings of the 33rd ACM International Conference on Multimedia, Co-Located with MM 2025
PB - Association for Computing Machinery, Inc
Y2 - 27 October 2025 through 31 October 2025
ER -