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In recent years, deep learning models have resulted in a huge amount of progress in various areas, including computer vision.
Are we ready for autonomous driving? the kitti vision benchmark suite
Geiger, A., Lenz, P., and 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., and Bengio, Y · 2014
Earlier work this paper cites.
Microsoft coco: Common objects in context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., and Zitnick, C. L · 2014
Earlier work this paper cites.
Imagenet large scale visual recognition challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., and et al · 2015
Earlier work this paper cites.
End to end learning for self-driving cars, 2016
Bojarski, M., Testa, D. D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., Jackel, L. D., Monfort, M., Muller, U., Zhang, J., Zhang, X., Zhao, J., and Zieba, K · 2016
Earlier work this paper cites.
The cityscapes dataset for semantic urban scene understanding
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., and Schiele, B · 2016
Earlier work this paper cites.
Virtual worlds as proxy for multi-object tracking analysis
Gaidon, A., Wang, Q., Cabon, Y., and Vig, E · 2016
Earlier work this paper cites.
FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation
Hoffman, J., Wang, D., Yu, F., and Darrell, T · 2016
Earlier work this paper cites.
Ssd: Single shot multibox detector
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., and Berg, A. C · 2016
Earlier work this paper cites.
Enet: A deep neural network architecture for real-time semantic segmentation, 2016
Paszke, A., Chaurasia, A., Kim, S., and Culurciello, E · 2016
Earlier work this paper cites.
Xnor-net: Imagenet classification using binary convolutional neural networks
Rastegari, M., Ordonez, V., Redmon, J., and Farhadi, A · 2016
Earlier work this paper cites.
Is faster r-cnn doing well for pedestrian detection?
Zhang, L., Lin, L., Liang, X., and He, K · 2016
Earlier work this paper cites.
Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J · 2016
Earlier work this paper cites.
CARLA: An open urban driving simulator
Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., and Koltun, V · 2017
Earlier work this paper cites.
CyCADA: Cycle-Consistent Adversarial Domain Adaptation
Hoffman, J., Tzeng, E., Park, T., Zhu, J.-Y., Isola, P., Saenko, K., Efros, A. A., and Darrell, T · 2017
Earlier work this paper cites.
Mobilenets: Efficient convolutional neural networks for mobile vision applications
Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H · 2017
Earlier work this paper cites.
Speed/accuracy trade-offs for modern convolutional object detectors
Huang, J., Rathod, V., Sun, C., Zhu, M., Korattikara, A., Fathi, A., Fischer, I., Wojna, Z., Song, Y., Guadarrama, S., and et al · 2017
Cited alongside, same era.
Driving in the Matrix: Can virtual worlds replace human-generated annotations for real world tasks?
Johnson-Roberson, M., Barto, C., Mehta, R., Sridhar, S. N., Rosaen, K., and Vasudevan, R · 2017
Cited alongside, same era.
Kang, D., Ma, Z., and Chan, A. B · 2017
Cited alongside, same era.
Faster r-cnn: Towards real-time object detection with region proposal networks
Ren, S., He, K., Girshick, R., and Sun, J · 2017
Cited alongside, same era.
Playing for benchmarks
Richter, S. R., Hayder, Z., and Koltun, V · 2017
Cited alongside, same era.
Simulating lidar point cloud for autonomous driving using real-world scenes and traffic flows
Fang, J., Yan, F., Zhao, T., Zhang, F., Zhou, D., Yang, R., Ma, Y., and Wang, L · 2018
Later among the works it cites.
Few-shot Object Detection via Feature Reweighting
Kang, B., Liu, Z., Wang, X., Yu, F., Feng, J., and Darrell, T · 2018
Later among the works it cites.
What Makes Good Synthetic Training Data for Learning Disparity and Optical Flow Estimation?
Mayer, N., Ilg, E., Fischer, P., Hazirbas, C., Cremers, D., Dosovitskiy, A., and Brox, T · 2018
Later among the works it cites.
Few-shot image recognition by predicting parameters from activations
Qiao, S., Liu, C., Shen, W., and Yuille, A · 2018
Later among the works it cites.
Deep learning for self-driving cars: chances and challenges
Rao, Q. and Frtunikj, J · 2018
Later among the works it cites.
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Sankaranarayanan, S., Balaji, Y., Jain, A., Lim, S. N., and Chellappa, R · 2017
Cited alongside, same era.
Fully convolutional networks for semantic segmentation
Shelhamer, E., Long, J., and Darrell, T · 2017
Cited alongside, same era.
Learning to Compare: Relation Network for Few-Shot Learning
Sung, F., Yang, Y., Zhang, L., Xiang, T., Torr, P. H. S., and Hospedales, T. M · 2017
Cited alongside, same era.
Tsirikoglou, A., Kronander, J., Wrenninge, M., and Unger, J · 2017
Cited alongside, same era.
Wu, B., Wan, A., Yue, X., and Keutzer, K · 2017
Cited alongside, same era.
Learning cross-modal deep representations for robust pedestrian detection
Xu, D., Ouyang, W., Ricci, E., Wang, X., and Sebe, N · 2017
Cited alongside, same era.
Augmented Reality Meets Computer Vision: Efficient Data Generation for Urban Driving Scenes
Abu Alhaija, H., Mustikovela, S. K., Mescheder, L., Geiger, A., and Rother, C · 2018
Cited alongside, same era.
Continuous trade-off optimization between fast and accurate deep face detectors
Soviany, P. and Ionescu, R. T · 2018
Later among the works it cites.
Multinet: Real-time joint semantic reasoning for autonomous driving
Teichmann, M., Weber, M., Zoellner, M., Cipolla, R., and Urtasun, R · 2018
Later among the works it cites.
Training deep networks with synthetic data: Bridging the reality gap by domain randomization
Tremblay, J., Prakash, A., Acuna, D., Brophy, M., Jampani, V., Anil, C., To, T., Cameracci, E., Boochoon, S., and Birchfield, S · 2018
Later among the works it cites.
Dynamic Graph CNN for Learning on Point Clouds
Wang, Y., Sun, Y., Liu, Z., Sarma, S. E., Bronstein, M. M., and Solomon, J. M · 2018
Later among the works it cites.
Synscapes: A Photorealistic Synthetic Dataset for Street Scene Parsing
Wrenninge, M. and Unger, J · 2018
Later among the works it cites.
DCAN: Dual Channel-Wise Alignment Networks for Unsupervised Scene Adaptation
Wu, Z., Han, X., Lin, Y. L., Uzunbas, M. G., Goldstein, T., Lim, S. N., and Davis, L. S · 2018
Later among the works it cites.
Bdd100k: A diverse driving video database with scalable annotation tooling
Yu, F., Xian, W., Chen, Y., Liu, F., Liao, M., Madhavan, V., and Darrell, T · 2018
Later among the works it cites.
Fully Convolutional Adaptation Networks for Semantic Segmentation
Zhang, Y., Qiu, Z., Yao, T., Liu, D., and Mei, T · 2018
Later among the works it cites.
nuscenes: A multimodal dataset for autonomous driving
Caesar, H., Bankiti, V., Lang, A. H., Vora, S., Liong, V. E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., and Beijbom, O · 2019
Closest in time.
Complex-YOLO: An Euler-Region-Proposal for Real-Time 3D Object Detection on Point Clouds
Simon, M., Milz, S., Amende, K., and Gross, H.-M · 2019
Closest in time.