@inproceedings{wang-etal-2025-unified,
title = "A Unified Agentic Framework for Evaluating Conditional Image Generation",
author = "Wang, Jifang and
Yang, Xue and
Wang, Longyue and
Xu, Zhenran and
Wang, Yiyu and
Wang, Yaowei and
Luo, Weihua and
Zhang, Kaifu and
Hu, Baotian and
Zhang, Min",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.620/",
doi = "10.18653/v1/2025.acl-long.620",
pages = "12626--12646",
ISBN = "979-8-89176-251-0",
abstract = "Conditional image generation has gained significant attention for its ability to personalize content. However, the field faces challenges in developing task-agnostic, reliable, and explainable evaluation metrics. This paper introduces CIGEval, a unified agentic framework for comprehensive evaluation of conditional image generation tasks. CIGEval utilizes large multimodal models (LMMs) as its core, integrating a multi-functional toolbox and establishing a fine-grained evaluation framework. Additionally, we synthesize evaluation trajectories for fine-tuning, empowering smaller LMMs to autonomously select appropriate tools and conduct nuanced analyses based on tool outputs. Experiments across seven prominent conditional image generation tasks demonstrate that CIGEval (GPT-4o version) achieves a high correlation of 0.4625 with human assessments, closely matching the inter-annotator correlation of 0.47. Notably, when implemented with 7B open-source LMMs using only 2.3K training trajectories, CIGEval surpasses the previous GPT-4o-based state-of-the-art method. These findings indicate that CIGEval holds great potential for automating evaluation of image generation tasks while maintaining human-level reliability."
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<abstract>Conditional image generation has gained significant attention for its ability to personalize content. However, the field faces challenges in developing task-agnostic, reliable, and explainable evaluation metrics. This paper introduces CIGEval, a unified agentic framework for comprehensive evaluation of conditional image generation tasks. CIGEval utilizes large multimodal models (LMMs) as its core, integrating a multi-functional toolbox and establishing a fine-grained evaluation framework. Additionally, we synthesize evaluation trajectories for fine-tuning, empowering smaller LMMs to autonomously select appropriate tools and conduct nuanced analyses based on tool outputs. Experiments across seven prominent conditional image generation tasks demonstrate that CIGEval (GPT-4o version) achieves a high correlation of 0.4625 with human assessments, closely matching the inter-annotator correlation of 0.47. Notably, when implemented with 7B open-source LMMs using only 2.3K training trajectories, CIGEval surpasses the previous GPT-4o-based state-of-the-art method. These findings indicate that CIGEval holds great potential for automating evaluation of image generation tasks while maintaining human-level reliability.</abstract>
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%0 Conference Proceedings
%T A Unified Agentic Framework for Evaluating Conditional Image Generation
%A Wang, Jifang
%A Yang, Xue
%A Wang, Longyue
%A Xu, Zhenran
%A Wang, Yiyu
%A Wang, Yaowei
%A Luo, Weihua
%A Zhang, Kaifu
%A Hu, Baotian
%A Zhang, Min
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F wang-etal-2025-unified
%X Conditional image generation has gained significant attention for its ability to personalize content. However, the field faces challenges in developing task-agnostic, reliable, and explainable evaluation metrics. This paper introduces CIGEval, a unified agentic framework for comprehensive evaluation of conditional image generation tasks. CIGEval utilizes large multimodal models (LMMs) as its core, integrating a multi-functional toolbox and establishing a fine-grained evaluation framework. Additionally, we synthesize evaluation trajectories for fine-tuning, empowering smaller LMMs to autonomously select appropriate tools and conduct nuanced analyses based on tool outputs. Experiments across seven prominent conditional image generation tasks demonstrate that CIGEval (GPT-4o version) achieves a high correlation of 0.4625 with human assessments, closely matching the inter-annotator correlation of 0.47. Notably, when implemented with 7B open-source LMMs using only 2.3K training trajectories, CIGEval surpasses the previous GPT-4o-based state-of-the-art method. These findings indicate that CIGEval holds great potential for automating evaluation of image generation tasks while maintaining human-level reliability.
%R 10.18653/v1/2025.acl-long.620
%U https://aclanthology.org/2025.acl-long.620/
%U https://doi.org/10.18653/v1/2025.acl-long.620
%P 12626-12646
Markdown (Informal)
[A Unified Agentic Framework for Evaluating Conditional Image Generation](https://aclanthology.org/2025.acl-long.620/) (Wang et al., ACL 2025)
ACL
- Jifang Wang, Xue Yang, Longyue Wang, Zhenran Xu, Yiyu Wang, Yaowei Wang, Weihua Luo, Kaifu Zhang, Baotian Hu, and Min Zhang. 2025. A Unified Agentic Framework for Evaluating Conditional Image Generation. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 12626–12646, Vienna, Austria. Association for Computational Linguistics.