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The rising importance of 3D understanding, pivotal in computer vision, autonomous driving, and robotics, is evident.
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2021
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T. Yin, X. Zhou, and P. Krahenbuhl, “Center-based 3d object detection and tracking,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2021, pp. 11 784–11 793
2021
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2021
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H. Xu, G. Ghosh, P.-Y. Huang, D. Okhonko, A. Aghajanyan, F. Metze, L. Zettlemoyer, and C. Feichtenhofer, “Videoclip: Contrastive pre-training for zero-shot video-text understanding,” in Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing , 2021, pp. 6787–6800
2021
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Z. Chen and L. Jing, “Multimodal semi-supervised learning for 3d objects,” in The British Machine Vision Conference (BMVC) , 2021
2021
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T. Schick and H. Schütze, “Exploiting cloze-questions for few-shot text classification and natural language inference,” in Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume , 2021, pp. 255–269
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I. O. Tolstikhin, N. Houlsby, A. Kolesnikov, L. Beyer, X. Zhai, T. Unterthiner, J. Yung, A. Steiner, D. Keysers, J. Uszkoreit et al. , “Mlp-mixer: An all-mlp architecture for vision,” Advances in neural information processing systems , vol. 34, pp. 24 261–24 272, 2021
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H. Zhao, L. Jiang, J. Jia, P. H. Torr, and V. Koltun, “Point transformer,” in Proceedings of the IEEE/CVF international conference on computer vision , 2021, pp. 16 259–16 268
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2022
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S. Lee, M. Jeon, I. Kim, Y. Xiong, and H. J. Kim, “Sagemix: Saliency-guided mixup for point clouds,” Advances in Neural Information Processing Systems , vol. 35, pp. 23 580–23 592, 2022
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Y. Zhao, H. Fei, W. Ji, J. Wei, M. Zhang, M. Zhang, and T.-S. Chua, “Generating visual spatial description via holistic 3D scene understanding,” in Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , 2023, pp. 7960–7977
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A. Xiao, J. Huang, D. Guan, X. Zhang, S. Lu, and L. Shao, “Unsupervised point cloud representation learning with deep neural networks: A survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2023
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H. Fei, Q. Liu, M. Zhang, M. Zhang, and T.-S. Chua, “Scene graph as pivoting: Inference-time image-free unsupervised multimodal machine translation with visual scene hallucination,” in Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , 2023, pp. 5980–5994
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X. Zhu, R. Zhang, B. He, Z. Guo, Z. Zeng, Z. Qin, S. Zhang, and P. Gao, “Pointclip v2: Prompting clip and gpt for powerful 3d open-world learning,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 2639–2650
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