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Recently, the self-supervised learning framework data2vec has shown inspiring performance for various modalities using a masked student-teacher approach.
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Armeni, I., Sener, O., Zamir, A.R., Jiang, H., Brilakis, I., Fischer, M., Savarese, S.: 3D Semantic Parsing of Large-Scale Indoor Spaces. In: IEEE Conference on Computer Vision and Pattern Recognition (2016)
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Dai, A., Chang, A.X., Savva, M., Halber, M., Funkhouser, T., Nießner, M.: Scannet: Richly-annotated 3d reconstructions of indoor scenes. In: Proceedings of the IEEE conference on computer vision and pattern recognition (2017)
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Qi, C.R., Yi, L., Su, H., Guibas, L.J.: PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space. In: Advances in Neural Information Processing Systems (2017)
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Behley, J., Garbade, M., Milioto, A., Quenzel, J., Behnke, S., Stachniss, C., Gall, J.: SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences. In: International Conference on Computer Vision (2019)
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Choy, C., Gwak, J., Savarese, S.: 4d spatio-temporal convnets: Minkowski convolutional neural networks. In: IEEE Conference on Computer Vision and Pattern Recognition (2019)
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Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: International Conference on Learning Representations (2019)
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Uy, M.A., Pham, Q.H., Hua, B.S., Nguyen, D.T., Yeung, S.K.: Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data. In: International Conference on Computer Vision (2019)
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Baevski, A., Zhou, Y., Mohamed, A., Auli, M.: wav2vec 2.0: A framework for self-supervised learning of speech representations. In: Neural Information Processing Systems (2020)
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Caesar, H., Bankiti, V., Lang, A.H., Vora, S., Liong, V.E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., Beijbom, O.: nuScenes: A multimodal dataset for autonomous driving. In: IEEE Conference on Computer Vision and Pattern Recognition (2020)
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Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A Simple Framework for Contrastive Learning of Visual Representations. In: International Conference on Machine Learning (2020)
2020
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Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. In: International Conference on Learning Representations (2020)
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Grill, J.B., Strub, F., Altché, F., Tallec, C., Richemond, P., Buchatskaya, E., Doersch, C., Avila Pires, B., Guo, Z., Gheshlaghi Azar, M., et al.: Bootstrap your own latent-a new approach to self-supervised learning. In: Neural Information Processing Systems (2020)
2020
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He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: IEEE Conference on Computer Vision and Pattern Recognition (2020)
Wang, H., Liu, Q., Yue, X., Lasenby, J., Kusner, M.J.: Unsupervised Point Cloud Pre-Training via Occlusion Completion. In: International Conference on Computer Vision (2021)
2021
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Zhang, P., Dai, X., Yang, J., Xiao, B., Yuan, L., Zhang, L., Gao, J.: Multi-Scale Vision Longformer: A New Vision Transformer for High-Resolution Image Encoding. In: International Conference on Computer Vision (2021)
2021
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Baevski, A., Hsu, W.N., Xu, Q., Babu, A., Gu, J., Auli, M.: Data2vec: A general framework for self-supervised learning in speech, vision and language. In: International Conference on Machine Learning (2022)
2022
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Bao, H., Dong, L., Piao, S., Wei, F.: BEiT: BERT pre-training of image transformers. In: International Conference on Learning Representations (2022)
2022
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2020
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Rao, Y., Lu, J., Zhou, J.: Global-Local Bidirectional Reasoning for Unsupervised Representation Learning of 3D Point Clouds. In: IEEE Conference on Computer Vision and Pattern Recognition (2020)
2020
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Sharma, C., Kaul, M.: Self-Supervised Few-Shot Learning on Point Clouds. In: Neural Information Processing Systems (2020)
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Tian, Y., Krishnan, D., Isola, P.: Contrastive Multiview Coding. In: European Conference on Computer Vision (2020)
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Tian, Y., Sun, C., Poole, B., Krishnan, D., Schmid, C., Isola, P.: What Makes for Good Views for Contrastive Learning? In: Neural Information Processing Systems (2020)
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 (2020)
2020
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Baruch, G., Chen, Z., Dehghan, A., Dimry, T., Feigin, Y., Fu, P., Gebauer, T., Joffe, B., Kurz, D., Schwartz, A., Shulman, E.: ARKitscenes - a diverse real-world dataset for 3d indoor scene understanding using mobile RGB-d data. In: Neural Information Processing Systems (2021)
2021
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Caron, M., Touvron, H., Misra, I., Jégou, H., Mairal, J., Bojanowski, P., Joulin, A.: Emerging properties in self-supervised vision transformers. In: International Conference on Computer Vision (2021)
2021
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Chen, C.F.R., Fan, Q., Panda, R.: CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification. In: International Conference on Computer Vision (2021)
2021
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Bardes, A., Ponce, J., LeCun, Y.: Vicreg: Variance-invariance-covariance regularization for self-supervised learning. In: International Conference on Learning Representations (2022)
2022
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He, K., Chen, X., Xie, S., Li, Y., Dollár, P., Girshick, R.: Masked autoencoders are scalable vision learners. In: IEEE Conference on Computer Vision and Pattern Recognition (2022)
2022
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Li, Y., Wu, C.Y., Fan, H., Mangalam, K., Xiong, B., Malik, J., Feichtenhofer, C.: MViTv2: Improved multiscale vision transformers for classification and detection. In: IEEE Conference on Computer Vision and Pattern Recognition (2022)
2022
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Liu, H., Cai, M., Lee, Y.J.: Masked Discrimination for Self-Supervised Learning on Point Clouds. In: European Conference on Computer Vision (2022)
2022
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Ma, X., Qin, C., You, H., Ran, H., Fu, Y.: Rethinking network design and local geometry in point cloud: A simple residual mlp framework. In: International Conference on Learning Representations (2022)
2022
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Pang, Y., Wang, W., Tay, F.E., Liu, W., Tian, Y., Yuan, L.: Masked autoencoders for point cloud self-supervised learning. In: European Conference on Computer Vision (2022)
2022
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Yu, X., Tang, L., Rao, Y., Huang, T., Zhou, J., Lu, J.: Point-bert: Pre-training 3d point cloud transformers with masked point modeling. In: IEEE Conference on Computer Vision and Pattern Recognition (2022)
2022
Later among the works it cites.
Zhang, R., Guo, Z., Gao, P., Fang, R., Zhao, B., Wang, D., Qiao, Y., Li, H.: Point-M2AE: Multi-scale masked autoencoders for hierarchical point cloud pre-training. In: Advances in Neural Information Processing Systems (2022)
2022
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Zhang, R., Guo, Z., Zhang, W., Li, K., Miao, X., Cui, B., Qiao, Y., Gao, P., Li, H.: Pointclip: Point cloud understanding by clip. In: IEEE Conference on Computer Vision and Pattern Recognition (2022)
2022
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Dong, R., Qi, Z., Zhang, L., Zhang, J., Sun, J., Ge, Z., Yi, L., Ma, K.: Autoencoders as Cross-Modal Teachers: Can Pretrained 2D Image Transformers Help 3D Representation Learning? In: International Conference on Learning Representations (2023)
2023
Closest in time.
Gao, M., Xing, C., Martín-Martín, R., Wu, J., Xiong, C., Xue, L., Xu, R., Niebles, J.C., Savarese, S.: Ulip: Learning a unified representation of language, images, and point clouds for 3d understanding. In: IEEE Conference on Computer Vision and Pattern Recognition (2023)
2023
Closest in time.
Schult, J., Engelmann, F., Hermans, A., Litany, O., Tang, S., Leibe, B.: Mask3D for 3D Semantic Instance Segmentation. In: IEEE International Conference on Robotics and Automation (2023)
2023
Closest in time.