[1] H. Bilen and A. Vedaldi. Weakly supervised deep detection networks. In CVPR, 2016.
[2] Z. Cai and N. Vasconcelos. Cascade r-cnn: Delving into high quality object detection. In CVPR, 2018.
[3] N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, and S. Zagoruyko. End-to-end object detection with transformers. In ECCV, 2020.
[4] S. Changpinyo, P. Sharma, N. Ding, and R. Soricut. Conceptual 12M: Pushing web-scale image-text pre-training to recognize long-tail visual concepts. In CVPR, 2021.
[5] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei. Imagenet: A large-scale hierarchical image database. In CVPR, 2009.
[6] K. Desai and J. Johnson. Virtex: Learning visual representations from textual annotations. In CVPR, 2021.
[7] Y. Fang, W. Wang, B. Xie, Q. Sun, L. Wu, X. Wang, T. Huang, X. Wang, and Y. Cao. Eva: Exploring the limits of masked visual representation learning at scale. In CVPR, 2023.
[8] G. Ghiasi, Y. Cui, A. Srinivas, R. Qian, T.-Y. Lin, E. D. Cubuk, Q. V. Le, and B. Zoph. Simple copy-paste is a strong data augmentation method for instance segmentation. In CVPR, 2021.
[9] R. Girshick. Fast r-cnn. In CVPR, 2015.
[10] R. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In CVPR, 2014.
[11] X. Gu, T.-Y. Lin, W. Kuo, and Y. Cui. Open-vocabulary object detection via vision and language knowledge distillation. In ICLR, 2021.
[12] A. Gupta, P. Dollar, and R. Girshick. Lvis: A dataset for large vocabulary instance segmentation. In CVPR, 2019.
[13] G. Han, J. Ma, S. Huang, L. Chen, R. Chellappa, and S.-F. Chang. Multimodal few-shot object detection with meta-learning based cross-modal prompting. arXiv preprint arXiv:2204.07841, 2022.
[14] K. He, X. Chen, S. Xie, Y. Li, P. Dollár, and R. Girshick. Masked autoencoders are scalable vision learners. In CVPR, 2022.
[15] K. He, G. Gkioxari, P. Dollár, and R. Girshick. Mask r-cnn. In ICCV, 2017.
[16] K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In CVPR, 2016.
[17] G. Hinton, O. Vinyals, J. Dean, et al. Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531, 2015.
[18] C. Jia, Y. Yang, Y. Xia, Y.-T. Chen, Z. Parekh, H. Pham, Q. Le, Y.-H. Sung, Z. Li, and T. Duerig. Scaling up visual and vision-language representation learning with noisy text supervision. In ICML, 2021.
[19] X. Jiao, Y. Yin, L. Shang, X. Jiang, X. Chen, L. Li, F. Wang, and Q. Liu. Tinybert: Distilling bert for natural language understanding. In EMNLP, 2020.
[20] A. Kamath, M. Singh, Y. LeCun, G. Synnaeve, I. Misra, and N. Carion. Mdetr-modulated detection for end-to-end multi-modal understanding. In CVPR, 2021.
[21] B. Kang, S. Xie, M. Rohrbach, Z. Yan, A. Gordo, J. Feng, and Y. Kalantidis. Decoupling representation and classifier for long-tailed recognition. In ICLR, 2019.
[22] R. Krishna, Y. Zhu, O. Groth, J. Johnson, K. Hata, J. Kravitz, S. Chen, Y. Kalantidis, L.-J. Li, D. A. Shamma, et al. Visual genome: Connecting language and vision using crowdsourced dense image annotations. IJCV, 2017.
[23] J. Kuen, F. Perazzi, Z. Lin, J. Zhang, and Y.-P. Tan. Scaling object detection by transferring classification weights. In CVPR, 2019.
[24] A. Kuznetsova, H. Rom, N. Alldrin, J. Uijlings, I. Krasin, J. Pont-Tuset, S. Kamali, S. Popov, M. Malloci, A. Kolesnikov, et al. The open images dataset v4. IJCV, 2020.
[25] D.-H. Lee et al. Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks. In Workshop on ICML, 2013.