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This paper uses a graphic engine to simulate a large amount of training data with free annotations.
Lloyd, S.: Least squares quantization in pcm. IEEE transactions on information theory 28
1982
Earlier work this paper cites.
Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. Journal of machine learning research 13
2012
Earlier work this paper cites.
2015
Earlier work this paper cites.
Wright, S.J.: Coordinate descent algorithms. Mathematical Programming 151
2015
Earlier work this paper cites.
Yang, L., Luo, P., Change Loy, C., Tang, X.: A large-scale car dataset for fine-grained categorization and verification. In: CVPR (2015)
2015
Earlier work this paper cites.
Gaidon, A., Wang, Q., Cabon, Y., Vig, E.: Virtual worlds as proxy for multi-object tracking analysis. In: CVPR (2016)
2016
Earlier work this paper cites.
Liu, H., Tian, Y., Yang, Y., Pang, L., Huang, T.: Deep relative distance learning: Tell the difference between similar vehicles. In: CVPR (2016)
2016
Earlier work this paper cites.
Liu, X., Liu, W., Ma, H., Fu, H.: Large-scale vehicle re-identification in urban surveillance videos. In: ICME (2016)
2016
Earlier work this paper cites.
Richter, S.R., Vineet, V., Roth, S., Koltun, V.: Playing for data: Ground truth from computer games. In: ECCV (2016)
2016
Earlier work this paper cites.
Ristani, E., Solera, F., Zou, R., Cucchiara, R., Tomasi, C.: Performance measures and a data set for multi-target, multi-camera tracking. In: ECCV Workshops (2016)
2016
Earlier work this paper cites.
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: CVPR (2016)
2016
Earlier work this paper cites.
Zheng, L., Bie, Z., Sun, Y., Wang, J., Su, C., Wang, S., Tian, Q.: Mars: A video benchmark for large-scale person re-identification. In: ECCV (2016)
2016
Earlier work this paper cites.
2017
Earlier work this paper cites.
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. In: NeurIPS (2017)
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
Shrivastava, A., Pfister, T., Tuzel, O., Susskind, J., Wang, W., Webb, R.: Learning from simulated and unsupervised images through adversarial training. In: CVPR (2017)
2017
Earlier work this paper cites.
Wang, Z., Tang, L., Liu, X., Yao, Z., Yi, S., Shao, J., Yan, J., Wang, S., Li, H., Wang, X.: Orientation invariant feature embedding and spatial temporal regularization for vehicle re-identification. In: ICCV (2017)
2017
Cited alongside, same era.
Zheng, Z., Zheng, L., Yang, Y.: Unlabeled samples generated by gan improve the person re-identification baseline in vitro. In: CVPR. pp. 3754–3762 (2017)
2017
Cited alongside, same era.
Bai, Y., Lou, Y., Gao, F., Wang, S., Wu, Y., Duan, L.Y.: Group-sensitive triplet embedding for vehicle reidentification. IEEE Transactions on Multimedia 20
2018
Cited alongside, same era.
Bak, S., Carr, P., Lalonde, J.F.: Domain adaptation through synthesis for unsupervised person re-identification. In: ECCV (2018)
2018
Cited alongside, same era.
Binkowski, M., Sutherland, D.J., Arbel, M., Gretton, A.: Demystifying mmd gans. In: ICLR (2018)
Chu, R., Sun, Y., Li, Y., Liu, Z., Zhang, C., Wei, Y.: Vehicle re-identification with viewpoint-aware metric learning. In: ICCV (2019)
2019
Closest in time.
Kar, A., Prakash, A., Liu, M.Y., Cameracci, E., Yuan, J., Rusiniak, M., Acuna, D., Torralba, A., Fidler, S.: Meta-sim: Learning to generate synthetic datasets. In: ICCV (2019)
2019
Closest in time.
Khorramshahi, P., Kumar, A., Peri, N., Rambhatla, S.S., Chen, J.C., Chellappa, R.: A dual path modelwith adaptive attention for vehicle re-identification. In: ICCV (2019)
2019
Closest in time.
2019
Closest in time.
Luo, H., Gu, Y., Liao, X., Lai, S., Jiang, W.: Bag of tricks and a strong baseline for deep person re-identification. In: CVPR Workshops (2019)
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2018
Cited alongside, same era.
Deng, W., Zheng, L., Ye, Q., Kang, G., Yang, Y., Jiao, J.: Image-image domain adaptation with preserved self-similarity and domain-dissimilarity for person re-identification. In: CVPR (2018)
2018
Cited alongside, same era.
Hoffman, J., Tzeng, E., Park, T., Zhu, J.Y., Isola, P., Saenko, K., Efros, A., Darrell, T.: Cycada: Cycle-consistent adversarial domain adaptation. In: ICML (2018)
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Liu, X., Zhang, S., Huang, Q., Gao, W.: Ram: a region-aware deep model for vehicle re-identification. In: ICME (2018)
2018
Cited alongside, same era.
Sakaridis, C., Dai, D., Van Gool, L.: Semantic foggy scene understanding with synthetic data. International Journal of Computer Vision pp. 1–20 (2018)
2018
Cited alongside, same era.
Sun, Y., Zheng, L., Yang, Y., Tian, Q., Wang, S.: Beyond part models: Person retrieval with refined part pooling (and a strong convolutional baseline). In: ECCV (2018)
2018
Cited alongside, same era.
Tremblay, J., Prakash, A., Acuna, D., Brophy, M., Jampani, V., Anil, C., To, T., Cameracci, E., Boochoon, S., Birchfield, S.: Training deep networks with synthetic data: Bridging the reality gap by domain randomization. In: CVPR Workshops (2018)
2018
Cited alongside, same era.
2019
Closest in time.
Peng, X., Bai, Q., Xia, X., Huang, Z., Saenko, K., Wang, B.: Moment matching for multi-source domain adaptation. In: ICCV (2019)
2019
Closest in time.
Ruiz, N., Schulter, S., Chandraker, M.: Learning to simulate. In: ICLR (2019)
2019
Closest in time.
Sun, X., Zheng, L.: Dissecting person re-identification from the viewpoint of viewpoint. In: CVPR (2019)
2019
Closest in time.
Tang, Z., Naphade, M., Birchfield, S., Tremblay, J., Hodge, W., Kumar, R., Wang, S., Yang, X.: Pamtri: Pose-aware multi-task learning for vehicle re-identification using highly randomized synthetic data. In: ICCV (2019)
2019
Closest in time.
Tang, Z., Naphade, M., Liu, M.Y., Yang, X., Birchfield, S., Wang, S., Kumar, R., Anastasiu, D., Hwang, J.N.: Cityflow: A city-scale benchmark for multi-target multi-camera vehicle tracking and re-identification. In: CVPR (2019)
2019
Closest in time.
Zheng, Z., Ruan, T., Wei, Y., Yang, Y.: Vehiclenet: Learning robust feature representation for vehicle re-identification. In: CVPR Workshops (2019)
2019
Closest in time.
2020
Closest in time.
Naphade, M., Wang, S., Anastasiu, D.C., Tang, Z., Chang, M.C., Yang, X., Zheng, L., Sharma, A., Chellappa, R., Chakraborty, P.: The 4th ai city challenge. In: CVPR Workshops. pp. 626–627 (2020)
2020
Closest in time.
2020
Closest in time.
Yao, Y., Plested, J., Gedeon, T.: Information-preserving feature filter for short-term eeg signals. Neurocomputing (2020)
2020
Closest in time.