Fetching the paper…
Reading the bibliography…
Rich semantic information extraction plays a vital role on next-generation intelligent vehicles.
H. Cho, Y.-W. Seo, B. V. Kumar, and R. R. Rajkumar, “A multi-sensor fusion system for moving object detection and tracking in urban driving environments,” in 2014 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2014, pp. 1836–1843
2014
Earlier work this paper cites.
W. Kuo, B. Hariharan, and J. Malik, “Deepbox: Learning objectness with convolutional networks,” in Proceedings of the IEEE international conference on computer vision , 2015, pp. 2479–2487
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
2016
Earlier work this paper cites.
M. Defferrard, X. Bresson, and P. Vandergheynst, “Convolutional neural networks on graphs with fast localized spectral filtering,” in Advances in neural information processing systems , 2016, pp. 3844–3852
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
C. Lu, R. Krishna, M. Bernstein, and L. Fei-Fei, “Visual relationship detection with language priors,” in European conference on computer vision . Springer, 2016, pp. 852–869
2016
Earlier work this paper cites.
T. N. Kipf and M. Welling, “Variational graph auto-encoders,” arXiv preprint arXiv:1611.07308 , 2016
2016
Earlier work this paper cites.
A. Dosovitskiy, G. Ros, F. Codevilla, A. Lopez, and V. Koltun, “CARLA: An open urban driving simulator,” in Proceedings of the 1st Annual Conference on Robot Learning , 2017, pp. 1–16
2017
Earlier work this paper cites.
F. Altché and A. de La Fortelle, “An lstm network for highway trajectory prediction,” in 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC) . IEEE, 2017, pp. 353–359
2017
Earlier work this paper cites.
J. Kim and J. Canny, “Interpretable learning for self-driving cars by visualizing causal attention,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 2942–2950
2017
Earlier work this paper cites.
A. Rasouli, I. Kotseruba, and J. K. Tsotsos, “Are they going to cross? a benchmark dataset and baseline for pedestrian crosswalk behavior,” in Proceedings of the IEEE International Conference on Computer Vision Workshops , 2017, pp. 206–213
2017
Earlier work this paper cites.
Z. Chen and X. Huang, “End-to-end learning for lane keeping of self-driving cars,” in 2017 IEEE Intelligent Vehicles Symposium (IV) . IEEE, 2017, pp. 1856–1860
2017
Earlier work this paper cites.
D. Xu, Y. Zhu, C. B. Choy, and L. Fei-Fei, “Scene graph generation by iterative message passing,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 5410–5419
2017
Earlier work this paper cites.
S. Gupta, J. Davidson, S. Levine, R. Sukthankar, and J. Malik, “Cognitive mapping and planning for visual navigation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 2616–2625
2017
Earlier work this paper cites.
Y. Li, W. Ouyang, B. Zhou, K. Wang, and X. Wang, “Scene graph generation from objects, phrases and region captions,” in Proceedings of the IEEE International Conference on Computer Vision , 2017, pp. 1261–1270
2017
Cited alongside, same era.
R. Krishna, Y. Zhu, O. Groth, J. Johnson, K. Hata, J. Kravitz, S. Chen, Y. Kalantidis, L.-J. Li, D. A. Shamma et al. , “Visual genome: Connecting language and vision using crowdsourced dense image annotations,” International journal of computer vision , vol. 123, no. 1, pp. 32–73, 2017
2017
Cited alongside, same era.
W. Maddern, G. Pascoe, C. Linegar, and P. Newman, “1 year, 1000 km: The oxford robotcar dataset,” The International Journal of Robotics Research , vol. 36, no. 1, pp. 3–15, 2017
2017
Cited alongside, same era.
2017
X. Li, X. Ying, and M. C. Chuah, “Grip: Graph-based interaction-aware trajectory prediction,” in 2019 IEEE Intelligent Transportation Systems Conference (ITSC) . IEEE, 2019, pp. 3960–3966
2019
Later among the works it cites.
J. F. Kooij, F. Flohr, E. A. Pool, and D. M. Gavrila, “Context-based path prediction for targets with switching dynamics,” International Journal of Computer Vision , vol. 127, no. 3, pp. 239–262, 2019
2019
Later among the works it cites.
L. Li, Z. Gan, Y. Cheng, and J. Liu, “Relation-aware graph attention network for visual question answering,” in Proceedings of the IEEE International Conference on Computer Vision , 2019, pp. 10 313–10 322
2019
Later among the works it cites.
J. Behley, M. Garbade, A. Milioto, J. Quenzel, S. Behnke, C. Stachniss, and J. Gall, “Semantickitti: A dataset for semantic scene understanding of lidar sequences,” in Proceedings of the IEEE International Conference on Computer Vision , 2019, pp. 9297–9307
2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
M. Simonovsky and N. Komodakis, “Dynamic edge-conditioned filters in convolutional neural networks on graphs,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 3693–3702
2017
Cited alongside, same era.
D. Xu, D. Anguelov, and A. Jain, “Pointfusion: Deep sensor fusion for 3d bounding box estimation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 244–253
2018
Cited alongside, same era.
J. Kim, A. Rohrbach, T. Darrell, J. Canny, and Z. Akata, “Textual explanations for self-driving vehicles,” in Proceedings of the European conference on computer vision (ECCV) , 2018, pp. 563–578
2018
Cited alongside, same era.
2018
Cited alongside, same era.
A. Carballo, S. Seiya, J. Lambert, H. Darweesh, P. Narksri, L. Y. Morales, N. Akai, E. Takeuchi, and K. Takeda, “End-to-end autonomous mobile robot navigation with model-based system support,” Journal of Robotics and Mechatronics , vol. 30, no. 4, pp. 563–583, 2018
2018
Cited alongside, same era.
V. Zambaldi, D. Raposo, A. Santoro, V. Bapst, Y. Li, I. Babuschkin, K. Tuyls, D. Reichert, T. Lillicrap, E. Lockhart et al. , “Deep reinforcement learning with relational inductive biases,” in International Conference on Learning Representations , 2018
2018
Cited alongside, same era.
M. Simonovsky and N. Komodakis, “Graphvae: Towards generation of small graphs using variational autoencoders,” in International Conference on Artificial Neural Networks . Springer, 2018, pp. 412–422
2018
Cited alongside, same era.
2018
Cited alongside, same era.
R. Kesten, M. Usman, J. Houston, T. Pandya, K. Nadhamuni, A. Ferreira, M. Yuan, B. Low, A. Jain, P. Ondruska et al. , “Lyft level 5 av dataset 2019,” urlhttps://level5. lyft. com/dataset , 2019
2019
Later among the works it cites.
M. Meyer and G. Kuschk, “Automotive radar dataset for deep learning based 3d object detection,” in 2019 16th European Radar Conference (EuRAD) . IEEE, 2019, pp. 129–132
2019
Later among the works it cites.
Y. Liang, Y. Bai, W. Zhang, X. Qian, L. Zhu, and T. Mei, “Vrr-vg: Refocusing visually-relevant relationships,” in Proceedings of the IEEE International Conference on Computer Vision , 2019, pp. 10 403–10 412
2019
Later among the works it cites.
A. Bendimerad, “Mining useful patterns in attributed graphs,” Ph.D. dissertation, Université de Lyon, 2019
2019
Later among the works it cites.
H. Caesar, V. Bankiti, A. H. Lang, S. Vora, V. E. Liong, Q. Xu, A. Krishnan, Y. Pan, G. Baldan, and O. Beijbom, “nuscenes: A multimodal dataset for autonomous driving,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 11 621–11 631
2020
Closest in time.
Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, and S. Y. Philip, “A comprehensive survey on graph neural networks,” IEEE Transactions on Neural Networks and Learning Systems , 2020
2020
Closest in time.
D. Chen, B. Zhou, V. Koltun, and P. Krähenbühl, “Learning by cheating,” in Conference on Robot Learning . PMLR, 2020, pp. 66–75
2020
Closest in time.
B. Wolfe, B. Seppelt, B. Mehler, B. Reimer, and R. Rosenholtz, “Rapid holistic perception and evasion of road hazards.” Journal of experimental psychology: general , vol. 149, no. 3, p. 490, 2020
2020
Closest in time.
P. Sun, H. Kretzschmar, X. Dotiwalla, A. Chouard, V. Patnaik, P. Tsui, J. Guo, Y. Zhou, Y. Chai, B. Caine et al. , “Scalability in perception for autonomous driving: Waymo open dataset,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 2446–2454
2020
Closest in time.
F. Yu, H. Chen, X. Wang, W. Xian, Y. Chen, F. Liu, V. Madhavan, and T. Darrell, “Bdd100k: A diverse driving dataset for heterogeneous multitask learning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 2636–2645
2020
Closest in time.