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The remarkable success of the autoregressive paradigm has made significant advancement in Multimodal Large Language Models (MLLMs), with powerful models like Show-o, Transfusion and Emu3 achieving notable progress in unified image understanding and generation.
Flickr30k entities: Collecting region-to-phrase correspondences for richer image-to-sentence models
Plummer, B. A., Wang, L., Cervantes, C. M., Caicedo, J. C., Hockenmaier, J., and Lazebnik, S · 2015
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Making the v in vqa matter: Elevating the role of image understanding in visual question answering
Goyal, Y., Khot, T., Summers-Stay, D., Batra, D., and Parikh, D · 2017
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J · 2018
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Gqa: A new dataset for real-world visual reasoning and compositional question answering
Hudson, D. A. and Manning, C. D · 2019
Earlier work this paper cites.
Learning transferable visual models from natural language supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al · 2021
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Hierarchical text-conditional image generation with clip latents
Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., and Chen, M · 2022
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High-resolution image synthesis with latent diffusion models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B · 2022
Earlier work this paper cites.
Dreamllm: Synergistic multimodal comprehension and creation
Dong, R., Han, C., Peng, Y., Qi, Z., Ge, Z., Yang, J., Zhao, L., Sun, J., Zhou, H., Wei, H., et al · 2023
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Mme: A comprehensive evaluation benchmark for multimodal large language models
Fu, C., Chen, P., Shen, Y., Qin, Y., Zhang, M., Lin, X., Yang, J., Zheng, X., Li, K., Sun, X., et al · 2023
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Tifa: Accurate and interpretable text-to-image faithfulness evaluation with question answering
Hu, Y., Liu, B., Kasai, J., Wang, Y., Ostendorf, M., Krishna, R., and Smith, N. A · 2023
Earlier work this paper cites.
Gemini: a family of highly capable multimodal models
Team, G., Anil, R., Borgeaud, S., Alayrac, J.-B., Yu, J., Soricut, R., Schalkwyk, J., Dai, A. M., Hauth, A., Millican, K., et al · 2023
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Next-gpt: Any-to-any multimodal llm
Wu, S., Fei, H., Qu, L., Ji, W., and Chua, T.-S · 2023
Cited alongside, same era.
Minigpt-4: Enhancing vision-language understanding with advanced large language models
Zhu, D., Chen, J., Shen, X., Li, X., and Elhoseiny, M · 2023
Cited alongside, same era.
Fluid: Scaling autoregressive text-to-image generative models with continuous tokens
Fan, L., Li, T., Qin, S., Li, Y., Sun, C., Rubinstein, M., Sun, D., He, K., and Tian, Y · 2024
Cited alongside, same era.
Seed-x: Multimodal models with unified multi-granularity comprehension and generation
Ge, Y., Zhao, S., Zhu, J., Ge, Y., Yi, K., Song, L., Li, C., Ding, X., and Shan, Y · 2024
Cited alongside, same era.
Geneval: An object-focused framework for evaluating text-to-image alignment
Tokenflow: Unified image tokenizer for multimodal understanding and generation
Qu, L., Zhang, H., Liu, Y., Wang, X., Jiang, Y., Gao, Y., Ye, H., Du, D. K., Yuan, Z., and Wu, X · 2024
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Direct preference optimization: Your language model is secretly a reward model
Rafailov, R., Sharma, A., Mitchell, E., Manning, C. D., Ermon, S., and Finn, C · 2024
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Llamafusion: Adapting pretrained language models for multimodal generation
Shi, W., Han, X., Zhou, C., Liang, W., Lin, X. V., Zettlemoyer, L., and Yu, L · 2024
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Any-to-any generation via composable diffusion
Tang, Z., Yang, Z., Zhu, C., Zeng, M., and Bansal, M · 2024
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Chameleon: Mixed-modal early-fusion foundation models
Team, C · 2024
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Ghosh, D., Hajishirzi, H., and Schmidt, L · 2024
Cited alongside, same era.
He, J., Lin, H., Wang, Q., Fung, Y., and Ji, H · 2024
Cited alongside, same era.
Ella: Equip diffusion models with llm for enhanced semantic alignment
Hu, X., Wang, R., Fang, Y., Fu, B., Cheng, P., and Yu, G · 2024
Cited alongside, same era.
Hurst, A., Lerer, A., Goucher, A. P., Perelman, A., Ramesh, A., Clark, A., Ostrow, A., Welihinda, A., Hayes, A., Radford, A., et al · 2024
Cited alongside, same era.
Li, H., Tian, C., Shao, J., Zhu, X., Wang, Z., Zhu, J., Dou, W., Wang, X., Li, H., Lu, L., et al · 2024
Cited alongside, same era.
Ma, Y., Liu, X., Chen, X., Liu, W., Wu, C., Wu, Z., Pan, Z., Xie, Z., Zhang, H., Zhao, L., et al · 2024
Cited alongside, same era.
Openai o1 system card
OpenAI · 2024
Cited alongside, same era.
Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models
Li, J., Li, D., Savarese, S., and Hoi, S
Cited in the paper.
Later among the works it cites.
Metamorph: Multimodal understanding and generation via instruction tuning
Tong, S., Fan, D., Zhu, J., Xiong, Y., Chen, X., Sinha, K., Rabbat, M., LeCun, Y., Xie, S., and Liu, Z · 2024
Later among the works it cites.
Emu3: Next-token prediction is all you need
Wang, X., Zhang, X., Luo, Z., Sun, Q., Cui, Y., Wang, J., Zhang, F., Wang, Y., Li, Z., Yu, Q., et al · 2024
Later among the works it cites.
X-vila: Cross-modality alignment for large language model
Ye, H., Huang, D.-A., Lu, Y., Yu, Z., Ping, W., Tao, A., Kautz, J., Han, S., Xu, D., Molchanov, P., et al · 2024
Later among the works it cites.
Mmmu: A massive multi-discipline multimodal understanding and reasoning benchmark for expert agi
Yue, X., Ni, Y., Zhang, K., Zheng, T., Liu, R., Zhang, G., Stevens, S., Jiang, D., Ren, W., Sun, Y., et al · 2024
Later among the works it cites.
Towards native generative model for 3d head avatar
Zhuang, Y., He, Y., Zhang, J., Wang, Y., Zhu, J., Yao, Y., Zhu, S., Cao, X., and Zhu, H · 2024
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Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning
Guo, D., Yang, D., Zhang, H., Song, J., Zhang, R., Xu, R., Zhu, Q., Ma, S., Wang, P., Bi, X., et al · 2025
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