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English Information

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Authors
# Name
1 Bruno Padilha(brunopadilha@usp.br)
2 João Ferreira(jef@ime.usp.br)

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Reference
# Reference
1 Du, Y., Zhao, Z., Song, Y., Zhao, Y., Su, F., Gong, T., and Meng, H. (2023). Strongsort: Make deepsort great again. IEEE Transactions on Multimedia.
2 Ferreira, J. E., Antônio Visintin, J., Okamoto, J., Cesar Bernardes, M., Paterlini, A., Roque, A. C., and Ramalho Miguel, M. (2018). Integrating the University of São Paulo security mobile app to the electronic monitoring system. In 2018 IEEE International Conference on Big Data (Big Data), pages 1377–1386. IEEE.
3 Fort, S., Ren, J., and Lakshminarayanan, B. (2021). Exploring the limits of out-of- distribution detection. Advances in Neural Information Processing Systems, 34:7068– 7081.
4 Guo, C., Pleiss, G., Sun, Y., and Weinberger, K. Q. (2017). On calibration of modern neural networks. In International conference on machine learning, pages 1321–1330. PMLR.
5 Hein, M., Andriushchenko, M., and Bitterwolf, J. (2019). Why relu networks yield high- confidence predictions far away from the training data and how to mitigate the prob- lem. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 41–50.
6 Hwang, Y., Jo, W., Hong, J., and Choi, Y. (2024). Overcoming overconfidence for active learning. IEEE Access.
7 Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., White- head, S., Berg, A. C., Lo, W.-Y., et al. (2023). Segment anything. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4015–4026.
8 Kristiadi, A., Hein, M., and Hennig, P. (2020). Being bayesian, even just a bit, fixes overconfidence in relu networks. In International conference on machine learning, pages 5436–5446. PMLR.
9 Nardi, E., Padilha, B., Kamaura, L., and Ferreira, J. (2022). Openimages cyclists: Ex- pandindo a generalização na detecção de ciclistas em câmeras de segurança. In Anais do XXXVII Simpósio Brasileiro de Bancos de Dados, pages 229–240, Porto Alegre, RS, Brasil. SBC.
10 Ren, J., Fort, S., Liu, J., Roy, A. G., Padhy, S., and Lakshminarayanan, B. (2021a). A simple fix to mahalanobis distance for improving near-ood detection. arXiv preprint arXiv:2106.09022.
11 Ren, P., Xiao, Y., Chang, X., Huang, P.-Y., Li, Z., Gupta, B. B., Chen, X., and Wang, X. (2021b). A survey of deep active learning. ACM computing surveys (CSUR), 54(9):1– 40.
12 Winkens, J., Bunel, R., Roy, A. G., Stanforth, R., Natarajan, V., Ledsam, J. R., MacWilliams, P., Kohli, P., Karthikesalingam, A., Kohl, S., et al. (2020). Contrastive training for improved out-of-distribution detection. arXiv preprint arXiv:2007.05566.
13 Zhang, C. and Ma, Y. (2012). Ensemble machine learning, volume 144. Springer.
14 Zhang, C.-B., Jiang, P.-T., Hou, Q., Wei, Y., Han, Q., Li, Z., and Cheng, M.-M. (2021). Delving deep into label smoothing. IEEE Transactions on Image Processing, 30:5984– 5996.
15 Zhang, X., Zhou, L., Xu, R., Cui, P., Shen, Z., and Liu, H. (2022). Towards unsupervised domain generalization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4910–4920.