A Spatio-Temporal Event Data Augmentation Method for Dynamic Vision Sensor

1. Amir, A., et al.: A low power, fully event-based gesture recognition system. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 7243–7252 (2017)

   Google Scholar 

2. Camunas-Mesa, L., Acosta-Jimenez, A., Serrano-Gotarredona, T., Linares-Barranco, B.: A digital pixel cell for address event representation image convolution processing. In: Bioengineered and Bioinspired Systems II. vol. 5839, pp. 160–171. SPIE (2005)

   Google Scholar 

3. Chan, V., Liu, S.C., van Schaik, A.: Aer ear: A matched silicon cochlea pair with address event representation interface. IEEE Transactions on Circuits and Systems I: Regular Papers 54(1), 48–59 (2007)

   Article  Google Scholar 

4. Cubuk, E.D., Zoph, B., Mane, D., Vasudevan, V., Le, Q.V.: Autoaugment: Learning augmentation strategies from data. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 113–123 (2019)

   Google Scholar 

5. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition. pp. 248–255. Ieee (2009)

   Google Scholar 

6. Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)

7. DeVries, T., Taylor, G.W.: Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552 (2017)

8. Gallego, G., et al.: Event-based vision: a survey. arXiv preprint arXiv:1904.08405 (2019)

9. Gehrig, D., Loquercio, A., Derpanis, K.G., Scaramuzza, D.: End-to-end learning of representations for asynchronous event-based data. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 5633–5643 (2019)

   Google Scholar 

10. Gu, F., Sng, W., Hu, X., Yu, F.: Eventdrop: data augmentation for event-based learning. arXiv preprint arXiv:2106.05836 (2021)

11. Hendrycks, D., Mu, N., Cubuk, E.D., Zoph, B., Gilmer, J., Lakshminarayanan, B.: Augmix: A simple data processing method to improve robustness and uncertainty. arXiv preprint arXiv:1912.02781 (2019)

12. Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images (2009)

    Google Scholar 

13. Lapique, L.: Recherches quantitatives sur l’excitation electrique des nerfs traitee comme une polarization. Journal of Physiology and Pathololgy 9, 620–635 (1907)

    Google Scholar 

14. Li, Y., Kim, Y., Park, H., Geller, T., Panda, P.: Neuromorphic data augmentation for training spiking neural networks. arXiv preprint arXiv:2203.06145 (2022)

15. Maass, W.: Networks of spiking neurons: the third generation of neural network models. Neural networks 10(9), 1659–1671 (1997)

    Article  Google Scholar 

16. Maass, W., Natschläger, T., Markram, H.: Real-time computing without stable states: A new framework for neural computation based on perturbations. Neural computation 14(11), 2531–2560 (2002)

    Article  MATH  Google Scholar 

17. Mahowald, M.A.: Vlsi analogs of neuronal visual processing: a synthesis of form and function (1992)

    Google Scholar 

18. Orchard, G., Jayawant, A., Cohen, G.K., Thakor, N.: Converting static image datasets to spiking neuromorphic datasets using saccades. Frontiers in neuroscience 9, 437 (2015)

    Article  Google Scholar 

19. Pak, M., Kim, S.: A review of deep learning in image recognition. In: 2017 4th International Conference on Computer Applications and Information Processing Technology (CAIPT). pp. 1–3 (2017). https://doi.org/10.1109/CAIPT.2017.8320684

20. Patrick, L., Posch, C., Delbruck, T.: A 128 \(\times \) 128 120 db 15\(\mu \) s latency asynchronous temporal contrast vision sensor. IEEE journal of solid-state circuits 43, 566–576 (2008)

    Article  Google Scholar 

21. Serrano-Gotarredona, T., Linares-Barranco, B.: A 128 \(\times \) 128 1.5% contrast sensitivity 0.9% fpn 3 \(\upmu \)s latency 4 mw asynchronous frame-free dynamic vision sensor using transimpedance preamplifiers. IEEE Journal of Solid-State Circuits 48(3), 827–838 (2013). https://doi.org/10.1109/JSSC.2012.2230553

22. Shorten, C., Khoshgoftaar, T.M.: A survey on image data augmentation for deep learning. Journal of big data 6(1), 1–48 (2019)

    Article  Google Scholar 

23. Sironi, A., Brambilla, M., Bourdis, N., Lagorce, X., Benosman, R.: Hats: Histograms of averaged time surfaces for robust event-based object classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 1731–1740 (2018)

    Google Scholar 

24. Stimberg, M., Brette, R., Goodman, D.F.: Brian 2, an intuitive and efficient neural simulator. Elife 8, e47314 (2019)

    Article  Google Scholar 

25. Vasudevan, A., Negri, P., Linares-Barranco, B., Serrano-Gotarredona, T.: Introduction and analysis of an event-based sign language dataset. In: 2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020). pp. 675–682. IEEE (2020)

    Google Scholar 

26. Wang, Q., Zhang, Y., Yuan, J., Lu, Y.: Space-time event clouds for gesture recognition: From rgb cameras to event cameras. In: 2019 IEEE Winter Conference on Applications of Computer Vision (WACV). pp. 1826–1835. IEEE (2019)

    Google Scholar 

27. Yun, S., Han, D., Oh, S.J., Chun, S., Choe, J., Yoo, Y.: Cutmix: Regularization strategy to train strong classifiers with localizable features. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 6023–6032 (2019)

    Google Scholar 

28. Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: mixup: Beyond empirical risk minimization. arXiv preprint arXiv:1710.09412 (2017)

29. Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE international conference on computer vision. pp.eps

    Google Scholar 

Download references