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With the emergence of large language models (LLMs) and vision foundation models, how to combine the intelligence and capacity of these open-sourced or API-available models to achieve open-world visual perception remains an open question.
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Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. Advances in neural information processing systems 33
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Aharon, S., Louis-Dupont, Ofri Masad, Yurkova, K., Lotem Fridman, Lkdci, Khvedchenya, E., Rubin, R., Bagrov, N., Tymchenko, B., Keren, T., Zhilko, A., Eran-Deci: Super-gradients (2021)
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Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., Sutskever, I.: Learning transferable visual models from natural language supervision (2021)
2021
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Ramesh, A., Pavlov, M., Goh, G., Gray, S., Voss, C., Radford, A., Chen, M., Sutskever, I.: Zero-shot text-to-image generation (2021)
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Alayrac, J.B., Donahue, J., Luc, P., Miech, A., Barr, I., Hasson, Y., Lenc, K., Mensch, A., Millican, K., Reynolds, M., Ring, R., Rutherford, E., Cabi, S., Han, T., Gong, Z., Samangooei, S., Monteiro, M., Menick, J., Borgeaud, S., Brock, A., Nematzadeh, A., Sharifzadeh, S., Binkowski, M., Barreira, R., Vinyals, O., Zisserman, A., Simonyan, K.: Flamingo: a visual language model for few-shot learning (2022)
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Bao, H., Wang, W., Dong, L., Liu, Q., Mohammed, O.K., Aggarwal, K., Som, S., Wei, F.: Vlmo: Unified vision-language pre-training with mixture-of-modality-experts (2022)
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Fu, T.J., Li, L., Gan, Z., Lin, K., Wang, W.Y., Wang, L., Liu, Z.: Violet : End-to-end video-language transformers with masked visual-token modeling (2022)
2022
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Mishra, S., Khashabi, D., Baral, C., Hajishirzi, H.: Cross-task generalization via natural language crowdsourcing instructions. In: 60th Annual Meeting of the Association for Computational Linguistics, ACL 2022. pp. 3470–3487. Association for Computational Linguistics (ACL) (2022)
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Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 10684–10695 (2022)
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Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., Le, Q.V., Zhou, D., et al.: Chain-of-thought prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems 35
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Betker, J., Goh, G., Jing, L., Brooks, T., Wang, J., Li, L., Ouyang, L., Zhuang, J., Lee, J., Guo, Y., Manassra, W., Dhariwal, P., Chu, C., Jiao, Y., Ramesh, A.: Improving image generation with better captions. https://cdn.openai.com/papers/dall-e-3.pdf (2023)
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Darcet, T., Oquab, M., Mairal, J., Bojanowski, P.: Vision transformers need registers (2023)
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Face, H.: Hugging face: An open-source community for machine learning (2023), https://huggingface.co/
2023
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Gao, J., Sarkar, B., Xia, F., Xiao, T., Wu, J., Ichter, B., Majumdar, A., Sadigh, D.: Physically grounded vision-language models for robotic manipulation (2023)
2023
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Grounded-SAM Contributors: Grounded-Segment-Anything (Apr 2023), https://github.com/IDEA-Research/Grounded-Segment-Anything
2023
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Jocher, G., Chaurasia, A., Qiu, J.: Yolov8 by ultralytics. https://github.com/ultralytics/ultralytics (2023)
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Jocher, G., Chaurasia, A., Qiu, J.: Ultralytics YOLO (Jan 2023), https://github.com/ultralytics/ultralytics
2023
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Shen, Y., Song, K., Tan, X., Li, D., Lu, W., Zhuang, Y.: Hugginggpt: Solving ai tasks with chatgpt and its friends in hugging face. In: Oh, A., Neumann, T., Globerson, A., Saenko, K., Hardt, M., Levine, S. (eds.) Advances in Neural Information Processing Systems. vol. 36, pp. 38154–38180. Curran Associates, Inc. (2023)
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Shen, Y., Song, K., Tan, X., Li, D., Lu, W., Zhuang, Y.: Hugginggpt: Solving ai tasks with chatgpt and its friends in hugging face (2023)
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Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., Rodriguez, A., Joulin, A., Grave, E., Lample, G.: Llama: Open and efficient foundation language models (2023)
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Kelly, C., Hu, L., Yang, C., Tian, Y., Yang, D., Yang, B., Huang, Z., Li, Z., Zou, Y.: Unifiedvisiongpt: Streamlining vision-oriented ai through generalized multimodal framework (2023)
2023
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2023
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Koh, J.Y., Fried, D., Salakhutdinov, R.R.: Generating images with multimodal language models. In: Oh, A., Neumann, T., Globerson, A., Saenko, K., Hardt, M., Levine, S. (eds.) Advances in Neural Information Processing Systems. vol. 36, pp. 21487–21506. Curran Associates, Inc. (2023)
2023
Cited alongside, same era.
Kojima, T., Gu, S.S., Reid, M., Matsuo, Y., Iwasawa, Y.: Large language models are zero-shot reasoners (2023)
2023
Cited alongside, same era.
Li, C.: Large multimodal models: Notes on cvpr 2023 tutorial. arXiv preprint arXiv:2306.14895 (2023)
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Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., Neubig, G.: Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. ACM Computing Surveys 55
2023
Cited alongside, same era.
Min, B., Ross, H., Sulem, E., Veyseh, A.P.B., Nguyen, T.H., Sainz, O., Agirre, E., Heintz, I., Roth, D.: Recent advances in natural language processing via large pre-trained language models: A survey. ACM Computing Surveys 56
2023
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Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., Bikel, D., Blecher, L., Ferrer, C.C., Chen, M., Cucurull, G., Esiobu, D., Fernandes, J., Fu, J., Fu, W., Fuller, B., Gao, C., Goswami, V., Goyal, N., Hartshorn, A., Hosseini, S., Hou, R., Inan, H., Kardas, M., Kerkez, V., Khabsa, M., Kloumann, I., Korenev, A., Koura, P.S., Lachaux, M.A., Lavril, T., Lee, J., Liskovich, D., Lu, Y., Mao, Y., Martinet, X., Mihaylov, T., Mishra, P., Molybog, I., Nie, Y., Poulton, A., Reizenstein, J., Rungta, R., Saladi, K., Schelten, A., Silva, R., Smith, E.M., Subramanian, R., Tan, X.E., Tang, B., Taylor, R., Williams, A., Kuan, J.X., Xu, P., Yan, Z., Zarov, I., Zhang, Y., Fan, A., Kambadur, M., Narang, S., Rodriguez, A., Stojnic, R., Edunov, S., Scialom, T.: Llama 2: Open foundation and fine-tuned chat models (2023)
2023
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Wang, W., Dai, J., Chen, Z., Huang, Z., Li, Z., Zhu, X., Hu, X., Lu, T., Lu, L., Li, H., et al.: Internimage: Exploring large-scale vision foundation models with deformable convolutions. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 14408–14419 (2023)
2023
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Wang, Z., Li, Y., Chen, X., Lim, S.N., Torralba, A., Zhao, H., Wang, S.: Detecting everything in the open world: Towards universal object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 11433–11443 (2023)
2023
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Wenlong, H., Chen, W., Ruohan, Z., Yunzhu, L., Jiajun, W., Li, F.F.: Voxposer: Composable 3d value maps for robotic manipulation with language models (2023)
2023
Later among the works it cites.
2023
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Wu, C., Yin, S., Qi, W., Wang, X., Tang, Z., Duan, N.: Visual chatgpt: Talking, drawing and editing with visual foundation models (2023)
2023
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Zhang, C., Han, D., Qiao, Y., Kim, J.U., Bae, S.H., Lee, S., Hong, C.S.: Faster segment anything: Towards lightweight sam for mobile applications (2023)
2023
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Zhao, X., Ding, W., An, Y., Du, Y., Yu, T., Li, M., Tang, M., Wang, J.: Fast segment anything (2023)
2023
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Zhu, D., Chen, J., Shen, X., Li, X., Elhoseiny, M.: Minigpt-4: Enhancing vision-language understanding with advanced large language models (2023)
2023
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Yang, D., Hu, L., Tian, Y., Li, Z., Kelly, C., Yang, B., Yang, C., Zou, Y.: Worldgpt: A sora-inspired video ai agent as rich world models from text and image inputs (2024)
2024
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