GenExam: A Multidisciplinary Text-to-Image Exam
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866910217742057472 |
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| author | Wang, Zhaokai Yin, Penghao Zhao, Xiangyu Tian, Changyao Qiao, Yu Wang, Wenhai Dai, Jifeng Luo, Gen |
| author_facet | Wang, Zhaokai Yin, Penghao Zhao, Xiangyu Tian, Changyao Qiao, Yu Wang, Wenhai Dai, Jifeng Luo, Gen |
| contents | Exams are a fundamental test of expert-level intelligence and require integrated understanding, reasoning, and generation. Existing exam-style benchmarks mainly focus on understanding and reasoning tasks, and current generation benchmarks emphasize the illustration of world knowledge and visual concepts, neglecting the evaluation of rigorous drawing exams. We introduce GenExam, the first benchmark for multidisciplinary text-to-image exams, featuring 1,000 samples across 10 subjects with exam-style prompts organized under a four-level taxonomy. Each problem is equipped with ground-truth images and fine-grained scoring points to enable a precise evaluation of semantic correctness and visual plausibility. Experiments on 17 text-to-image and unified models demonstrate the great challenge of GenExam and the huge gap where open-source models consistently lag behind the leading closed-source ones. By framing image generation as an exam, GenExam offers a rigorous assessment of models' ability to integrate understanding, reasoning, and generation, providing insights for on the path to intelligent generative models. Our benchmark and evaluation code are released at https://github.com/OpenGVLab/GenExam. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_14232 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GenExam: A Multidisciplinary Text-to-Image Exam Wang, Zhaokai Yin, Penghao Zhao, Xiangyu Tian, Changyao Qiao, Yu Wang, Wenhai Dai, Jifeng Luo, Gen Computer Vision and Pattern Recognition Exams are a fundamental test of expert-level intelligence and require integrated understanding, reasoning, and generation. Existing exam-style benchmarks mainly focus on understanding and reasoning tasks, and current generation benchmarks emphasize the illustration of world knowledge and visual concepts, neglecting the evaluation of rigorous drawing exams. We introduce GenExam, the first benchmark for multidisciplinary text-to-image exams, featuring 1,000 samples across 10 subjects with exam-style prompts organized under a four-level taxonomy. Each problem is equipped with ground-truth images and fine-grained scoring points to enable a precise evaluation of semantic correctness and visual plausibility. Experiments on 17 text-to-image and unified models demonstrate the great challenge of GenExam and the huge gap where open-source models consistently lag behind the leading closed-source ones. By framing image generation as an exam, GenExam offers a rigorous assessment of models' ability to integrate understanding, reasoning, and generation, providing insights for on the path to intelligent generative models. Our benchmark and evaluation code are released at https://github.com/OpenGVLab/GenExam. |
| title | GenExam: A Multidisciplinary Text-to-Image Exam |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.14232 |