Icme 2026 Grand Challenge on Scientific Image Quality Assessment

Zhang, Zicheng, Li, Wenzhe, Chen, Liang, Wang, Junying, Wen, Farong, Guo, Yiji, Shen, Ye, Yan, Qihang, Li, Chunyi, Zhang, Wenlong, Zhou, Wei, Liu, Xiaohong, Min, Xiongkuo and Zhai, Guangtao 2026. Icme 2026 Grand Challenge on Scientific Image Quality Assessment. Presented at: International Conference on Multimedia and Expo Workshops, Bangkok, Thailand, 5-9 July 2026. Proceedings of ICMEW 2016. IEEE, 10.1109/icmew69720.2026.11720703

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Abstract

The rapid integration of artificial intelligence into scientific workflows has increased the need for reliable Scientific Image Quality Assessment (SIQA). This paper presents the ICME 2026 Grand Challenge on SIQA, which benchmarks the capabilities of current large models to evaluate scientific imagery across four key dimensions: Validity, Completeness, Clarity, and Conformity. The challenge comprises two tracks: SIQA-U (Understanding), emphasizing multi-dimensional reasoning, and SIQA-S (Scoring), focusing on alignment with expert human judgment. Using the SIQA dataset, which spans diverse scientific domains and contains high-quality expert annotations, the competition attracts participation from seven international teams. Our analysis of submitted methods reveals a notable trend toward supervised fine-tuning and ensemble-based MLLM architectures. While top-performing models exhibit strong correlation with human perceptual scores, the results underscore a persistent gap in deep scientific reasoning.

Item Type:	Conference or Workshop Item - published (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Schools > Computational & Mathematical Sciences Schools > Computer Science & Informatics
Additional Information:	RRS policy applied
Publisher:	IEEE
ISBN:	9798331562359
ISSN:	2330-7927
Date of First Compliant Deposit:	20 October 2026
Last Modified:	20 Oct 2026 09:30
URI:	https://orca.cardiff.ac.uk/id/eprint/190279

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