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By combining natural language understanding, generation capabilities, and breadth of knowledge of large language models with image perception, recent large vision language models (LVLMs) have shown unprecedented visual reasoning capabilities.
1912
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Sidorov, O., Hu, R., Rohrbach, M., Singh, A.: Textcaps: a dataset for image captioning with reading comprehension. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part II 16. pp. 742–758. Springer (2020)
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Xie, S., Gu, J., Guo, D., Qi, C.R., Guibas, L., Litany, O.: Pointcontrast: Unsupervised pre-training for 3d point cloud understanding. In: European Conference on Computer Vision. pp. 574–591. Springer (2020)
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2022
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Hu, E.J., yelong shen, Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: LoRA: Low-rank adaptation of large language models. In: International Conference on Learning Representations (2022), https://openreview.net/forum?id=nZeVKeeFYf9
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Li, L.H., Zhang, P., Zhang, H., Yang, J., Li, C., Zhong, Y., Wang, L., Yuan, L., Zhang, L., Hwang, J.N., et al.: Grounded language-image pre-training. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 10965–10975 (2022)
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2023
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Yan, S., Song, C., Kong, Y., Huang, Q.: Multi-view representation is what you need for point-cloud pre-training. In: The Twelfth International Conference on Learning Representations (2023)
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Yan, S., Yang, Z., Li, H., Song, C., Guan, L., Kang, H., Hua, G., Huang, Q.: Implicit autoencoder for point-cloud self-supervised representation learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 14530–14542 (2023)
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Zhang, Y., Mai, Y., Roberts, J.S.R., Bommasani, R., Dubois, Y., Liang, P.: Helm instruct: A multidimensional instruction following evaluation framework with absolute ratings, https://crfm.stanford.edu/2024/02/18/helm-instruct.html
2024
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