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Neuromorphic spike data, an upcoming modality with high temporal resolution, has shown promising potential in autonomous driving by mitigating the challenges posed by high-velocity motion blur.
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J. Zhao, R. Xiong, H. Liu, J. Zhang, and T. Huang, “Spk2imgnet: Learning to reconstruct dynamic scene from continuous spike stream,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 11 996–12 005
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2021
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Cited alongside, same era.
J.-H. Lee and C.-S. Kim, “Monocular depth estimation using relative depth maps,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 9729–9738
2019
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M. Ramamonjisoa and V. Lepetit, “Sharpnet: Fast and accurate recovery of occluding contours in monocular depth estimation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops , Oct 2019
2019
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C. Godard, O. Mac Aodha, M. Firman, and G. J. Brostow, “Digging into self-supervised monocular depth estimation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 3828–3838
2019
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A. Pilzer, S. Lathuiliere, N. Sebe, and E. Ricci, “Refine and distill: Exploiting cycle-inconsistency and knowledge distillation for unsupervised monocular depth estimation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 9768–9777
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S. Zhao, H. Fu, M. Gong, and D. Tao, “Geometry-aware symmetric domain adaptation for monocular depth estimation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 9788–9798
2019
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2020
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M. Ramamonjisoa, Y. Du, and V. Lepetit, “Predicting sharp and accurate occlusion boundaries in monocular depth estimation using displacement fields,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2020
2020
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L. Zhu, S. Dong, J. Li, T. Huang, and Y. Tian, “Retina-like visual image reconstruction via spiking neural model,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 1438–1446
2020
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2021
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W. Wang, Y. Cao, J. Zhang, F. He, Z.-J. Zha, Y. Wen, and D. Tao, “Exploring sequence feature alignment for domain adaptive detection transformers,” in Proceedings of the 29th ACM International Conference on Multimedia , 2021, pp. 1730–1738
2021
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H. Sim, J. Oh, and M. Kim, “Xvfi: Extreme video frame interpolation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 14 489–14 498
2021
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J. Li, X. Wang, L. Zhu, J. Li, T. Huang, and Y. Tian, “Retinomorphic object detection in asynchronous visual streams,” 2022
2022
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J. Zhang, L. Tang, Z. Yu, J. Lu, and T. Huang, “Spike transformer: Monocular depth estimation for spiking camera,” in Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part VII . Springer, 2022, pp. 34–52
2022
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Y. Wang, J. Li, L. Zhu, X. Xiang, T. Huang, and Y. Tian, “Learning stereo depth estimation with bio-inspired spike cameras,” in 2022 IEEE International Conference on Multimedia and Expo (ICME) , 2022, pp. 1–6
2022
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2022
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L. Zhu, S. Dong, J. Li, T. Huang, and Y. Tian, “Ultra-high temporal resolution visual reconstruction from a fovea-like spike camera via spiking neuron model,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2022
2022
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Y. Wang, J. Li, L. Zhu, X. Xiang, T. Huang, and Y. Tian, “Learning stereo depth estimation with bio-inspired spike cameras,” in 2022 IEEE International Conference on Multimedia and Expo (ICME) . IEEE, 2022, pp. 1–6
2022
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2023
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2078
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