SridBench: Benchmark of Scientific Research Illustration Drawing of Image Generation Model

V. J. Reddi, C. Cheng, D. Kanter, P. Mattson, G. Schmuelling, C.-J. Wu, B. Anderson, M. Breughe, M. Charlebois, W. Chou, R. Chukka, C. Coleman, S. Davis, P. Deng, G. Diamos, J. Duke, D. Fick, J. S. Gardner, I. Hubara, S. Idgunji, T. B. Jablin, J. Jiao, T. S. John, P. Kanwar, D. Lee, J. Liao, A. Lokhmotov, F. Massa, P. Meng, P. Micikevicius, C. Osborne, G. Pekhimenko, A. T. R. Rajan, D. Sequeira, A. Sirasao, F. Sun, H. Tang, M. Thomson, F. Wei, E. Wu, L. Xu, K. Yamada, B. Yu, G. Yuan, A. Zhong, P. Zhang, and Y. Zhou, “Mlperf inference benchmark,” 2020. [Online]. Available: https://arxiv.org/abs/1911.02549

Original

1911

Earlier work this paper cites.

J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” 2020. [Online]. Available: https://arxiv.org/abs/2006.11239

Original

2006

Earlier work this paper cites.

N. Metzger, “Dsm refinement with deep encoder-decoder networks,” 2020. [Online]. Available: https://arxiv.org/abs/2012.07427

Original

2012

Earlier work this paper cites.

R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” in CVPR , 2022, pp. 10 684–10 695

2022

Earlier work this paper cites.

A. Ramesh, P. Dhariwal, A. Nichol, C. Chu, and M. Chen, “Hierarchical text-conditional image generation with clip latents,” arXiv preprint arXiv:2204.06125 , vol. 1, no. 2, p. 3, 2022

Original

2022

Earlier work this paper cites.

J. Wei, X. Wang, D. Schuurmans, M. Bosma, F. Xia, E. Chi, Q. V. Le, D. Zhou et al. , “Chain-of-thought prompting elicits reasoning in large language models,” NeurIPS , 2022

2022

Earlier work this paper cites.

J. Betker, G. Goh, L. Jing, T. Brooks, J. Wang, L. Li, L. Ouyang, J. Zhuang, J. Lee, Y. Guo et al. , “Improving image generation with better captions,” Computer Science. https://cdn. openai. com/papers/dall-e-3. pdf , vol. 2, no. 3, p. 8, 2023

2023

Earlier work this paper cites.

D. Podell, Z. English, K. Lacey, A. Blattmann, T. Dockhorn, J. Müller, J. Penna, and R. Rombach, “Sdxl: Improving latent diffusion models for high-resolution image synthesis,” arXiv preprint arXiv:2307.01952 , 2023

Original

2023

Earlier work this paper cites.

K. Lin, Z. Yang, L. Li, J. Wang, and L. Wang, “Designbench: Exploring and benchmarking dall-e 3 for imagining visual design,” 2023. [Online]. Available: https://arxiv.org/abs/2310.15144

Original

2023

Earlier work this paper cites.

Q. Sun, Y. Cui, X. Zhang, F. Zhang, Q. Yu, Z. Luo, Y. Wang, Y. Rao, J. Liu, T. Huang, and X. Wang, “Generative multimodal models are in-context learners,” 2023

2023

Earlier work this paper cites.

K. Huang, K. Sun, E. Xie, Z. Li, and X. Liu, “T2i-compbench: A comprehensive benchmark for open-world compositional text-to-image generation,” Advances in Neural Information Processing Systems , vol. 36, pp. 78 723–78 747, 2023

2023

Cited alongside, same era.

A. Singh, P. Agarwal, Z. Huang, A. Singh, T. Yu, S. Kim, V. Bursztyn, N. Vlassis, and R. A. Rossi, “Figcaps-hf: A figure-to-caption generative framework and benchmark with human feedback,” 2023. [Online]. Available: https://arxiv.org/abs/2307.10867

Original

2023

Cited alongside, same era.

G. Team, R. Anil, S. Borgeaud, J.-B. Alayrac, J. Yu, R. Soricut, J. Schalkwyk, A. M. Dai, A. Hauth, K. Millican et al. , “Gemini: a family of highly capable multimodal models,” arXiv preprint arXiv:2312.11805 , 2023

Original

2023

Cited alongside, same era.

P. Esser, S. Kulal, A. Blattmann, R. Entezari, J. Müller, H. Saini, Y. Levi, D. Lorenz, A. Sauer, F. Boesel et al. , “Scaling rectified flow transformers for high-resolution image synthesis,” in Forty-first International Conference on Machine Learning , 2024

P. Wang, S. Bai, S. Tan, S. Wang, Z. Fan, J. Bai, K. Chen, X. Liu, J. Wang, W. Ge, Y. Fan, K. Dang, M. Du, X. Ren, R. Men, D. Liu, C. Zhou, J. Zhou, and J. Lin, “Qwen2-vl: Enhancing vision-language model’s perception of the world at any resolution,” arXiv preprint arXiv:2409.12191 , 2024

Original

2024

Later among the works it cites.

Z. Chen, J. Wu, W. Wang, W. Su, G. Chen, S. Xing, M. Zhong, Q. Zhang, X. Zhu, L. Lu et al. , “Internvl: Scaling up vision foundation models and aligning for generic visual-linguistic tasks,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 24 185–24 198

2024

Later among the works it cites.

Z. Chen, W. Wang, Y. Cao, Y. Liu, Z. Gao, E. Cui, J. Zhu, S. Ye, H. Tian, Z. Liu et al. , “Expanding performance boundaries of open-source multimodal models with model, data, and test-time scaling,” arXiv preprint arXiv:2412.05271 , 2024

Original

2024

Later among the works it cites.

Z. Xu, S. Du, Y. Qi, C. Xu, C. Yuan, and J. Guo, “Chartbench: A benchmark for complex visual reasoning in charts,” 2024. [Online]. Available: https://arxiv.org/abs/2312.15915