GIE-Bench: Towards Grounded Evaluation for Text-Guided Image Editing
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866912500738424832 |
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| author | Qian, Yusu Lu, Jiasen Fu, Tsu-Jui Wang, Xinze Chen, Chen Yang, Yinfei Hu, Wenze Gan, Zhe |
| author_facet | Qian, Yusu Lu, Jiasen Fu, Tsu-Jui Wang, Xinze Chen, Chen Yang, Yinfei Hu, Wenze Gan, Zhe |
| contents | Editing images using natural language instructions has become a natural and expressive way to modify visual content; yet, evaluating the performance of such models remains challenging. Existing evaluation approaches often rely on image-text similarity metrics like CLIP, which lack precision. In this work, we introduce a new benchmark designed to evaluate text-guided image editing models in a more grounded manner, along two critical dimensions: (i) functional correctness, assessed via automatically generated multiple-choice questions that verify whether the intended change was successfully applied; and (ii) image content preservation, which ensures that non-targeted regions of the image remain visually consistent using an object-aware masking technique and preservation scoring. The benchmark includes over 1000 high-quality editing examples across 20 diverse content categories, each annotated with detailed editing instructions, evaluation questions, and spatial object masks. We conduct a large-scale study comparing GPT-Image-1, the latest flagship in the text-guided image editing space, against several state-of-the-art editing models, and validate our automatic metrics against human ratings. Results show that GPT-Image-1 leads in instruction-following accuracy, but often over-modifies irrelevant image regions, highlighting a key trade-off in the current model behavior. GIE-Bench provides a scalable, reproducible framework for advancing more accurate evaluation of text-guided image editing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_11493 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GIE-Bench: Towards Grounded Evaluation for Text-Guided Image Editing Qian, Yusu Lu, Jiasen Fu, Tsu-Jui Wang, Xinze Chen, Chen Yang, Yinfei Hu, Wenze Gan, Zhe Computer Vision and Pattern Recognition Editing images using natural language instructions has become a natural and expressive way to modify visual content; yet, evaluating the performance of such models remains challenging. Existing evaluation approaches often rely on image-text similarity metrics like CLIP, which lack precision. In this work, we introduce a new benchmark designed to evaluate text-guided image editing models in a more grounded manner, along two critical dimensions: (i) functional correctness, assessed via automatically generated multiple-choice questions that verify whether the intended change was successfully applied; and (ii) image content preservation, which ensures that non-targeted regions of the image remain visually consistent using an object-aware masking technique and preservation scoring. The benchmark includes over 1000 high-quality editing examples across 20 diverse content categories, each annotated with detailed editing instructions, evaluation questions, and spatial object masks. We conduct a large-scale study comparing GPT-Image-1, the latest flagship in the text-guided image editing space, against several state-of-the-art editing models, and validate our automatic metrics against human ratings. Results show that GPT-Image-1 leads in instruction-following accuracy, but often over-modifies irrelevant image regions, highlighting a key trade-off in the current model behavior. GIE-Bench provides a scalable, reproducible framework for advancing more accurate evaluation of text-guided image editing. |
| title | GIE-Bench: Towards Grounded Evaluation for Text-Guided Image Editing |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.11493 |