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Recent developments for Semi-Supervised Object Detection (SSOD) have shown the promise of leveraging unlabeled data to improve an object detector.
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: Proceedings of the European Conference on Computer Vision (ECCV) (2014)
2014
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Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. In: Advances in neural information processing systems (NeurIPS). pp. 91–99 (2015)
2015
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Sajjadi, M., Javanmardi, M., Tasdizen, T.: Regularization with stochastic transformations and perturbations for deep semi-supervised learning. In: Advances in Neural Information Processing Systems (NeurIPS). pp. 1163–1171 (2016)
2016
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2017
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Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. In: Proceedings of the International Conference on Learning Representations (ICLR) (2017)
2017
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Krasin, I., Duerig, T., Alldrin, N., Ferrari, V., Abu-El-Haija, S., Kuznetsova, A., Rom, H., Uijlings, J., Popov, S., Kamali, S., Malloci, M., Pont-Tuset, J., Veit, A., Belongie, S., Gomes, V., Gupta, A., Sun, C., Chechik, G., Cai, D., Feng, Z., Narayanan, D., Murphy, K.: Openimages: A public dataset for large-scale multi-label and multi-class image classification. Dataset available from https://storage.googleapis.com/openimages/web/index.html (2017)
2017
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Laine, S., Aila, T.: Temporal ensembling for semi-supervised learning. In: Proceedings of the International Conference on Learning Representations (ICLR) (2017)
2017
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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 (CVPR) (2017)
2017
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Tarvainen, A., Valpola, H.: Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. In: Advances in neural information processing systems (NeurIPS). pp. 1195–1204 (2017)
2017
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Lee, K., Lee, K., Lee, H., Shin, J.: A simple unified framework for detecting out-of-distribution samples and adversarial attacks. In: Advances in Neural Information Processing Systems (NeurIPS) (2018)
2018
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Liang, S., Li, Y., Srikant, R.: Enhancing the reliability of out-of-distribution image detection in neural networks. In: Proceedings of the International Conference on Learning Representations (ICLR) (2018)
2018
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Pidhorskyi, S., Almohsen, R., Adjeroh, D.A., Doretto, G.: Generative probabilistic novelty detection with adversarial autoencoders. In: Advances in Neural Information Processing Systems (NeurIPS) (2018)
2018
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Sabokrou, M., Khalooei, M., Fathy, M., Adeli, E.: Adversarially learned one-class classifier for novelty detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)
2018
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Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: mixup: Beyond empirical risk minimization. In: Proc. International Conference on Learning Representations (ICLR) (2018)
2018
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Zong, B., Song, Q., Min, M.R., Cheng, W., Lumezanu, C., Cho, D., Chen, H.: Deep autoencoding gaussian mixture model for unsupervised anomaly detection. In: Proceedings of the International Conference on Learning Representations (ICLR) (2018)
2018
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Berthelot, D., Carlini, N., Goodfellow, I., Papernot, N., Oliver, A., Raffel, C.A.: Mixmatch: A holistic approach to semi-supervised learning. In: Advances in Neural Information Processing Systems (NeurIPS). pp. 5049–5059 (2019)
2019
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2019
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Guo, H., Mao, Y., Zhang, R.: Mixup as locally linear out-of-manifold regularization. In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI). vol. 33, pp. 3714–3722 (2019)
2019
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Hendrycks, D., Mazeika, M., Dietterich, T.: Deep anomaly detection with outlier exposure. In: Proceedings of the International Conference on Learning Representations (ICLR) (2019)
2019
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Jeong, J., Lee, S., Kim, J., Kwak, N.: Consistency-based semi-supervised learning for object detection. In: Advances in Neural Information Processing Systems (NeurIPS) (2019)
2019
Earlier work this paper cites.
Nalisnick, E., Matsukawa, A., Teh, Y.W., Gorur, D., Lakshminarayanan, B.: Do deep generative models know what they don’t know? In: Proceedings of the International Conference on Learning Representations (ICLR) (2019)
2019
Cited alongside, same era.
Wu, Y., Kirillov, A., Massa, F., Lo, W.Y., Girshick, R.: Detectron2. https://github.com/facebookresearch/detectron2 (2019)
2019
Cited alongside, same era.
Yun, S., Han, D., Oh, S.J., Chun, S., Choe, J., Yoo, Y.: Cutmix: Regularization strategy to train strong classifiers with localizable features. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV). pp. 6023–6032 (2019)
2019
Cited alongside, same era.
Dhamija, A., Gunther, M., Ventura, J., Boult, T.: The overlooked elephant of object detection: Open set. In: Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV) (2020)
2020
Cited alongside, same era.
2021
Later among the works it cites.
Joseph, K., Khan, S., Khan, F.S., Balasubramanian, V.N.: Towards open world object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2021)
2021
Later among the works it cites.
Liu, Y.C., Ma, C.Y., He, Z., Kuo, C.W., Chen, K., Zhang, P., Wu, B., Kira, Z., Vajda, P.: Unbiased teacher for semi-supervised object detection. In: Proceedings of the International Conference on Learning Representations (ICLR) (2021)
2021
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2021
Later among the works it cites.
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Hendrycks, D., Mu, N., Cubuk, E.D., Zoph, B., Gilmer, J., Lakshminarayanan, B.: AugMix: A simple data processing method to improve robustness and uncertainty. Proceedings of the International Conference on Learning Representations (ICLR) (2020)
2020
Cited alongside, same era.
Hsu, Y.C., Shen, Y., Jin, H., Kira, Z.: Generalized odin: Detecting out-of-distribution image without learning from out-of-distribution data. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2020)
2020
Cited alongside, same era.
Liu, W., Wang, X., Owens, J.D., Li, Y.: Energy-based out-of-distribution detection. In: Advances in Neural Information Processing Systems (NeurIPS) (2020)
2020
Cited alongside, same era.
Mohseni, S., Pitale, M., Yadawa, J., Wang, Z.: Self-supervised learning for generalizable out-of-distribution detection. In: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) (2020)
2020
Cited alongside, same era.
Sohn, K., Berthelot, D., Li, C.L., Zhang, Z., Carlini, N., Cubuk, E.D., Kurakin, A., Zhang, H., Raffel, C.: Fixmatch: Simplifying semi-supervised learning with consistency and confidence. In: Advances in Neural Information Processing Systems (NeurIPS) (2020)
2020
Cited alongside, same era.
2020
Cited alongside, same era.
2020
Cited alongside, same era.
Yu, Q., Ikami, D., Irie, G., Aizawa, K.: Multi-task curriculum framework for open-set semi-supervised learning. In: Proceedings of the European Conference on Computer Vision (ECCV) (2020)
2020
Cited alongside, same era.
2021
Later among the works it cites.
2021
Later among the works it cites.
Saito, K., Kim, D., Saenko, K.: Openmatch: Open-set consistency regularization for semi-supervised learning with outliers. In: Advances in Neural Information Processing Systems (NeurIPS) (2021)
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.
2021
Later among the works it cites.
Tian, J., Yung, D., Hsu, Y.C., Kira, Z.: A geometric perspective towards neural calibration via sensitivity decomposition. In: Advances in Neural Information Processing Systems (NeurIPS) (2021)
2021
Later among the works it cites.
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 Conference on Computer Vision and Pattern Recognition (CVPR) (2021)
2021
Later among the works it cites.
Zareian, A., Rosa, K.D., Hu, D.H., Chang, S.F.: Open-vocabulary object detection using captions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2021)
2021
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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 Conference on Computer Vision and Pattern Recognition (CVPR) (2021)
2021
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2022
Closest in time.
Gu, X., Lin, T.Y., Kuo, W., Cui, Y.: Open-vocabulary object detection via vision and language knowledge distillation. In: Proceedings of the International Conference on Learning Representations (ICLR) (2022)
2022
Closest in time.
Kim, D., Lin, T.Y., Angelova, A., Kweon, I.S., Kuo, W.: Learning open-world object proposals without learning to classify. IEEE Robotics and Automation Letters (2022)
2022
Closest in time.