[35] Z. Liu, H. Ding, H. Zhong, W. Li, J. Dai, and C. He. Influence selection for active learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9274–9283, 2021. 8, 17
[36] D. J. MacKay. Information-based objective functions for active data selection. Neural computation, 4(4):590-604, 1992. 3
[37] R. Mahmood, S. Fidler, and M. T. Law. Low budget active learning via wasserstein distance: An integer programming approach. arXiv preprint arXiv:2106.02968, 2021. 3
[38] A. Ng, M. Jordan, and Y. Weiss. On spectral clustering: Analysis and an algorithm. Advances in neural information processing systems, 14, 2001. 6, 15
[39] M. Patrick, Y. M. Asano, P. Kuznetsova, R. Fong, J. F. Henriques, G. Zweig, and A. Vedaldi. Multi-modal self-supervision from generalized data transformations. arXiv preprint arXiv:2003.04298, 2020. 3
[40] A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748-8763. PMLR, 2021. 10
[41] M. Raghu, T. Unterthiner, S. Kornblith, C. Zhang, and A. Dosovitskiy. Do vision transformers see like convolutional neural networks? In Thirty-Fifth Conference on Neural Information Processing Systems, 2021. 4
[42] S. Ramalingam, D. Glasner, K. Patel, R. Vemulapalli, S. Jayasumana, and S. Kumar. Less is more: Selecting informative and diverse subsets with balancing constraints. arXiv preprint arXiv:2104.12835, 2021. 1, 3
[43] P. Ren, Y. Xiao, X. Chang, P.-Y. Huang, Z. Li, X. Chen, and X. Wang. A survey of deep active learning. arXiv preprint arXiv:2009.00236, 2020. 1
[44] O. Sener and S. Savarese. Active learning for convolutional neural networks: A core-set approach. arXiv preprint arXiv:1708.00489, 2017. 1, 3, 4, 8, 9
[45] B. Settles. Active learning literature survey. 2009. 1
[46] K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014. 7, 17
[47] S. Sinha, S. Ebrahimi, and T. Darrell. Variational adversarial active learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5972-5981, 2019. 3, 8, 17
[48] Y. Tian, C. Sun, B. Poole, D. Krishnan, C. Schmid, and P. Isola. What makes for good views for contrastive learning? arXiv preprint arXiv:2005.10243, 2020. 3
[49] Z. Tong, Y. Song, J. Wang, and L. Wang. Videomae: Masked autoencoders are data-efficient learners for self-supervised video pre-training. Advances in neural information processing systems, 35:10078-10093, 2022. 3
[50] H. Touvron, M. Cord, M. Douze, F. Massa, A. Sablayrolles, and H. Jégou. Training data-efficient image transformers & distillation through attention. In International Conference on Machine Learning, pages 10347-10357. PMLR, 2021. 4, 6, 9, 10, 16, 17
[51] X. Wang, L. Lian, and S. X. Yu. Unsupervised selective labeling for more effective semi-supervised learning. In European Conference on Computer Vision, pages 427-445. Springer, 2022. 3
[52] C. Wei, H. Fan, S. Xie, C.-Y. Wu, A. Yuille, and C. Feichtenhofer. Masked feature prediction for self-supervised visual pre-training. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14668-14678, 2022. 3
[53] T. Xiao, X. Wang, A. A. Efros, and T. Darrell. What should not be contrastive in contrastive learning. arXiv preprint arXiv:2008.05659, 2020. 3