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State-of-the-art pedestrian detection models have achieved great success in many benchmarks.
The PASCAL Visual Object Classes Challenge 2007 (VOC2007) Results
Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: · 2007
Earlier work this paper cites.
Monocular pedestrian detection: Survey and experiments
Enzweiler, M., Gavrila, D.M.: · 2009
Earlier work this paper cites.
Pedestrian detection: A benchmark
Dollár, P., Wojek, C., Schiele, B., Perona, P.: · 2009
Earlier work this paper cites.
Pedestrian detection: An evaluation of the state of the art
Dollar, P., Wojek, C., Schiele, B., Perona, P.: · 2012
Earlier work this paper cites.
Pedestrian detection: An evaluation of the state of the art
Dollar, P., Wojek, C., Schiele, B., Perona, P.: · 2012
Earlier work this paper cites.
Are we ready for autonomous driving? the kitti vision benchmark suite
Geiger, A., Lenz, P., Urtasun, R.: · 2012
Earlier work this paper cites.
Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: · 2014
Earlier work this paper cites.
Spatial pyramid pooling in deep convolutional networks for visual recognition
He, K., Zhang, X., Ren, S., Sun, J.: · 2014
Earlier work this paper cites.
Conditional generative adversarial nets
Mirza, M., Osindero, S.: · 2014
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Simonyan, K., Zisserman, A.: · 2014
Earlier work this paper cites.
Learning scene-specific pedestrian detectors without real data
Hattori, H., Naresh Boddeti, V., Kitani, K.M., Kanade, T.: · 2015
Earlier work this paper cites.
Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., Chintala, S.: · 2015
Earlier work this paper cites.
Deep generative image models using a laplacian pyramid of adversarial networks
Denton, E.L., Chintala, S., Fergus, R., et al.: · 2015
Earlier work this paper cites.
How far are we from solving pedestrian detection?
Zhang, S., Benenson, R., Omran, M., Hosang, J., Schiele, B.: · 2016
Cited alongside, same era.
Yolo9000: better, faster, stronger
Redmon, J., Farhadi, A.: · 2016
Cited alongside, same era.
Lcrowdv: Generating labeled videos for simulation-based crowd behavior learning
Cheung, E., Wong, T.K., Bera, A., Wang, X., Manocha, D.: · 2016
Cited alongside, same era.
Image-to-image translation with conditional adversarial networks
Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: · 2016
Cited alongside, same era.
Context encoders: Feature learning by inpainting
Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., Efros, A.A.: · 2016
Cited alongside, same era.
Faster r-cnn: Towards real-time object detection with region proposal networks
Ren, S., He, K., Girshick, R., Sun, J.: · 2017
Later among the works it cites.
STD-PD: Generating synthetic training data for pedestrian detection in unannotated videos
Cheung, E.C., Wong, T.K., Bera, A., Manocha, D.: · 2017
Later among the works it cites.
Least squares generative adversarial networks
Xudong, M., Qing, L., Xie, H., Raymond, Y.K., L., Zhen, W., Stephen, Paul, S.: · 2017
Later among the works it cites.
Arjovsky, M., Chintala, S., Bottou, L.: · 2017
Later among the works it cites.
Mmd gan: Towards deeper understanding of moment matching network
Li, C.L., Chang, W.C., Cheng, Y., Yang, Y., Póczos, B.: · 2017
Later among the works it cites.
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Ledig, C., Theis, L., Huszár, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., et al.: · 2016
Cited alongside, same era.
The cityscapes dataset for semantic urban scene understanding
Marius, C., Mohamed, O., Sebastian, R., Timo, R., Markus, E., Rodrigo, B., Uwe, F., Stefan, R., Bernt, S.: · 2016
Cited alongside, same era.
A new benchmark for vision-based cyclist detection
Li, X., Flohr, F., Yang, Y., Xiong, H., Braun, M., Pan, S., Li, K., Gavrila, D.M.: · 2016
Cited alongside, same era.
A unified multi-scale deep convolutional neural network for fast object detection
Cai, Z., Fan, Q., Feris, R.S., Vasconcelos, N.: · 2016
Cited alongside, same era.
Is faster r-cnn doing well for pedestrian detection?
Zhang, L., Lin, L., Liang, X., He, K.: · 2016
Cited alongside, same era.
Generative adversarial networks as variational training of energy based models
Zhai, S., Cheng, Y., Feris, R.S., Zhang, Z.: · 2016
Cited alongside, same era.
Semi-supervised learning with context-conditional generative adversarial networks
Denton, E., Gross, S., Fergus, R.: · 2016
Cited alongside, same era.
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.: · 2017
Later among the works it cites.
Semantic image inpainting with deep generative models
Yeh, R.A., Chen, C., Lim, T.Y., Schwing, A.G., Hasegawa-Johnson, M., Do, M.N.: · 2017
Later among the works it cites.
Unsupervised image-to-image translation networks
Liu, M.Y., Breuel, T., Kautz, J.: · 2017
Later among the works it cites.
Unpaired image-to-image translation using cycle-consistent adversarial networks
Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: · 2017
Later among the works it cites.
Pose guided person image generation
Ma, L., Jia, X., Sun, Q., Schiele, B., Tuytelaars, T., Van Gool, L.: · 2017
Later among the works it cites.
Unlabeled samples generated by gan improve the person re-identification baseline in vitro
Zheng, Z., Zheng, L., Yang, Y.: · 2017
Later among the works it cites.
Joint deep learning for pedestrian detection
Ouyang, W., Wang, X.: · 2063
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