Fetching the paper…
Reading the bibliography…
To date, the most powerful semi-supervised object detectors (SS-OD) are based on pseudo-boxes, which need a sequence of post-processing with fine-tuned hyper-parameters.
Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL Visual Object Classes Challenge 2012 (VOC2012) Results. http://www.pascal-network.org/challenges/VOC/voc2012/workshop/index.html
2012
Earlier work this paper cites.
Bachman, P., Alsharif, O., Precup, D.: Learning with pseudo-ensembles. Advances in neural information processing systems 27
2014
Earlier work this paper cites.
Bachman, P., Alsharif, O., Precup, D.: Learning with pseudo-ensembles. Advances in neural information processing systems 27
2014
Earlier work this paper cites.
Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft coco: Common objects in context. In: European conference on computer vision. pp. 740–755. Springer (2014)
2014
Earlier work this paper cites.
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research 15
2014
Earlier work this paper cites.
Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 28
2015
Earlier work this paper cites.
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision (IJCV) 115
2015
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 770–778 (2016)
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
Sajjadi, M., Javanmardi, M., Tasdizen, T.: Regularization with stochastic transformations and perturbations for deep semi-supervised learning. Advances in neural information processing systems 29
2016
Earlier work this paper cites.
Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 2117–2125 (2017)
2017
Earlier work this paper cites.
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision. pp. 2980–2988 (2017)
2017
Earlier work this paper cites.
Tarvainen, A., Valpola, H.: Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. Advances in neural information processing systems 30
2017
Cited alongside, same era.
Miyato, T., Maeda, S.i., Koyama, M., Ishii, S.: Virtual adversarial training: a regularization method for supervised and semi-supervised learning. IEEE transactions on pattern analysis and machine intelligence 41
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2019
Cited alongside, same era.
Xie, Q., Luong, M.T., Hovy, E., Le, Q.V.: Self-training with noisy student improves imagenet classification. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 10687–10698 (2020)
2020
Later among the works it cites.
Zhang, S., Chi, C., Yao, Y., Lei, Z., Li, S.Z.: Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 9759–9768 (2020)
2020
Later among the works it cites.
Ge, Z., Hu, C., Huang, X., Qiu, B., Yoshie, O.: Dualbox: Generating bbox pair with strong correspondence via occlusion pattern clustering and proposal refinement. In: 2020 25th International Conference on Pattern Recognition (ICPR). pp. 2097–2102. IEEE (2021)
2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Berthelot, D., Carlini, N., Goodfellow, I., Papernot, N., Oliver, A., Raffel, C.A.: Mixmatch: A holistic approach to semi-supervised learning. Advances in Neural Information Processing Systems 32
2019
Cited alongside, same era.
Jeong, J., Lee, S., Kim, J., Kwak, N.: Consistency-based semi-supervised learning for object detection. Advances in neural information processing systems 32
2019
Cited alongside, same era.
Tian, Z., Shen, C., Chen, H., He, T.: Fcos: Fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 9627–9636 (2019)
2019
Cited alongside, same era.
Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International conference on machine learning. pp. 1597–1607. PMLR (2020)
2020
Cited alongside, same era.
Ge, Z., Jie, Z., Huang, X., Xu, R., Yoshie, O.: Ps-rcnn: Detecting secondary human instances in a crowd via primary object suppression. In: 2020 IEEE international conference on multimedia and expo (ICME). pp. 1–6. IEEE (2020)
2020
Cited alongside, same era.
Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., Yang, J.: Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33
2020
Cited alongside, same era.
Sohn, K., Berthelot, D., Carlini, N., Zhang, Z., Zhang, H., Raffel, C.A., Cubuk, E.D., Kurakin, A., Li, C.L.: Fixmatch: Simplifying semi-supervised learning with consistency and confidence. Advances in Neural Information Processing Systems 33
2020
Cited alongside, same era.
2020
Cited alongside, same era.
2021
Later among the works it cites.
2021
Later among the works it cites.
Tang, Y., Chen, W., Luo, Y., Zhang, Y.: Humble teachers teach better students for semi-supervised object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 3132–3141 (2021)
2021
Later among the works it cites.
Xu, M., Zhang, Z., Hu, H., Wang, J., Wang, L., Wei, F., Bai, X., Liu, Z.: End-to-end semi-supervised object detection with soft teacher. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 3060–3069 (2021)
2021
Later among the works it cites.
Yang, Q., Wei, X., Wang, B., Hua, X.S., Zhang, L.: Interactive self-training with mean teachers for semi-supervised object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 5941–5950 (2021)
2021
Later among the works it cites.
2021
Later among the works it cites.
Zhixing, D., Zhang, R., Chang, M., Liu, S., Chen, T., Chen, Y., et al.: Distilling object detectors with feature richness. Advances in Neural Information Processing Systems 34
2021
Later among the works it cites.
Zhou, Q., Yu, C., Wang, Z., Qian, Q., Li, H.: Instant-teaching: An end-to-end semi-supervised object detection framework. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 4081–4090 (2021)
2021
Later among the works it cites.