SSTN: Self-Supervised Domain Adaptation Thermal Object Detection for Autonomous Driving

T. Hastie, R. Tibshirani, and J. Friedman, “The elements of statistical learning. springer series in statistics,” in : . Springer, 2001

2001

Earlier work this paper cites.

L. Smith and M. Gasser, “The development of embodied cognition: Six lessons from babies,” Artificial life , vol. 11, no. 1-2, pp. 13–29, 2005

2005

Earlier work this paper cites.

G. E. Hinton, S. Osindero, and Y.-W. Teh, “A fast learning algorithm for deep belief nets,” Neural computation , vol. 18, no. 7, pp. 1527–1554, 2006

2006

Earlier work this paper cites.

R. Hadsell, S. Chopra, and Y. LeCun, “Dimensionality reduction by learning an invariant mapping,” in 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06) , vol. 2. IEEE, 2006, pp. 1735–1742

2006

Earlier work this paper cites.

R. Salakhutdinov and G. Hinton, “Deep boltzmann machines,” in Artificial intelligence and statistics . PMLR, 2009, pp. 448–455

2009

Earlier work this paper cites.

H. E. Den Ouden, P. Kok, and F. P. De Lange, “How prediction errors shape perception, attention, and motivation,” Frontiers in psychology , vol. 3, p. 548, 2012

2012

Earlier work this paper cites.

W. Li, D. Zheng, T. Zhao, and M. Yang, “An effective approach to pedestrian detection in thermal imagery,” in 2012 8th International Conference on Natural Computation . IEEE, 2012, pp. 325–329

2012

Earlier work this paper cites.

D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” arXiv preprint arXiv:1312.6114 , 2013

Original

2013

Earlier work this paper cites.

J. Hohwy, The predictive mind . Oxford University Press, 2013

2013

Earlier work this paper cites.

I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial networks,” arXiv preprint arXiv:1406.2661 , 2014

Original

2014

Earlier work this paper cites.

T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft coco: Common objects in context,” in European conference on computer vision . Springer, 2014, pp. 740–755

2014

Earlier work this paper cites.

S. Ren, K. He, R. B. Girshick, and J. Sun, “Faster r-cnn: towards real-time object detection with region proposal networks. corr abs/1506.01497 (2015),” arXiv preprint arXiv:1506.01497 , 2015

Original

2015

Earlier work this paper cites.

R. Soundrapandiyan and P. C. Mouli, “Adaptive pedestrian detection in infrared images using background subtraction and local thresholding,” Procedia Computer Science , vol. 58, pp. 706–713, 2015

2015

Earlier work this paper cites.

C. Doersch, A. Gupta, and A. A. Efros, “Unsupervised visual representation learning by context prediction,” in Proceedings of the IEEE international conference on computer vision , 2015, pp. 1422–1430

2015

Earlier work this paper cites.

S. Hwang, J. Park, N. Kim, Y. Choi, and I. So Kweon, “Multispectral pedestrian detection: Benchmark dataset and baseline,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 1037–1045

2015

Earlier work this paper cites.

W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg, “Ssd: Single shot multibox detector,” in European conference on computer vision . Springer, 2016, pp. 21–37

2016

Earlier work this paper cites.

M. Noroozi and P. Favaro, “Unsupervised learning of visual representations by solving jigsaw puzzles,” in European conference on computer vision . Springer, 2016, pp. 69–84

2016

Cited alongside, same era.

R. Zhang, P. Isola, and A. A. Efros, “Colorful image colorization,” in European conference on computer vision . Springer, 2016, pp. 649–666

2016

Cited alongside, same era.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778

2016

Cited alongside, same era.

J. Baek, S. Hong, J. Kim, and E. Kim, “Efficient pedestrian detection at nighttime using a thermal camera,” Sensors , vol. 17, no. 8, p. 1850, 2017

2017

Cited alongside, same era.

A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” arXiv preprint arXiv:1706.03762 , 2017

F. Yu, D. Wang, Y. Chen, N. Karianakis, P. Yu, D. Lymberopoulos, and X. Chen, “Unsupervised domain adaptation for object detection via cross-domain semi-supervised learning,” arXiv preprint arXiv:1911.07158 , 2019

Original

2019

Later among the works it cites.

T. Chen, X. Zhai, M. Ritter, M. Lucic, and N. Houlsby, “Self-supervised gans via auxiliary rotation loss,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 12 154–12 163

2019

Later among the works it cites.

X. Ji, J. F. Henriques, and A. Vedaldi, “Invariant information clustering for unsupervised image classification and segmentation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 9865–9874

2019

Later among the works it cites.

M. Ye, X. Zhang, P. C. Yuen, and S.-F. Chang, “Unsupervised embedding learning via invariant and spreading instance feature,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 6210–6219

2019