データ拡張 (Data Augmentation) を学習中に使い分けるRefined Data Augmentationについて解説しました。
He, Zhuoxun, et al. "Data augmentation revisited: Rethinking the distribution gap between clean and augmented data." arXiv preprint arXiv:1909.09148 (2019).
[DL輪読会]Neural Radiance Flow for 4D View Synthesis and Video Processing (NeRF...Deep Learning JP
Neural Radiance Flow (NeRFlow) is a method that extends Neural Radiance Fields (NeRF) to model dynamic scenes from video data. NeRFlow simultaneously learns two fields - a radiance field to reconstruct images like NeRF, and a flow field to model how points in space move over time using optical flow. This allows it to generate novel views from a new time point. The model is trained end-to-end by minimizing losses for color reconstruction from volume rendering and optical flow reconstruction. However, the method requires training separate models for each scene and does not generalize to unknown scenes.
データ拡張 (Data Augmentation) を学習中に使い分けるRefined Data Augmentationについて解説しました。
He, Zhuoxun, et al. "Data augmentation revisited: Rethinking the distribution gap between clean and augmented data." arXiv preprint arXiv:1909.09148 (2019).
[DL輪読会]Neural Radiance Flow for 4D View Synthesis and Video Processing (NeRF...Deep Learning JP
Neural Radiance Flow (NeRFlow) is a method that extends Neural Radiance Fields (NeRF) to model dynamic scenes from video data. NeRFlow simultaneously learns two fields - a radiance field to reconstruct images like NeRF, and a flow field to model how points in space move over time using optical flow. This allows it to generate novel views from a new time point. The model is trained end-to-end by minimizing losses for color reconstruction from volume rendering and optical flow reconstruction. However, the method requires training separate models for each scene and does not generalize to unknown scenes.
This document contains contact information for several researchers from the Machine Perception and Robotics Group at Chubu University in Japan, including professors, lecturers, and research assistants. It lists their names, titles, contact details such as phone numbers and email addresses, and web links for the group's website. The group is part of the Department of Robotics Science and Technology or Department of Computer Science within the College of Engineering at Chubu University.
This document contains contact information for several researchers from the Machine Perception and Robotics Group at Chubu University in Japan, including professors, lecturers, and research assistants. It lists their names, titles, contact details such as phone numbers and email addresses, and web links for the group's website. The group is part of the Department of Robotics Science and Technology or Department of Computer Science within the College of Engineering at Chubu University.
The document contains a date, 1615, repeated twice. There is no other text or context provided, so a summary is not possible based on the extremely limited information given.
36. 参考文献
• [Burt&Adelson83] P. J. Burt, E. H. Adelson, "A Multiresolution Spline With Application to Image Mosaics", ACM Transactions on
Graphics, vol. 2, no. 4, pp. 217-236, 1983.
• [Perez+03] P. Pérez, M. Gangnet, A. Blake, "Poisson Image Editing", ACM Transactions on Graphics (SIGGRAPH'03), vol. 22, no.
3, pp. 313-318, 2003.
• [Xue+12] S. Xue, A. Agarwala, J. Dorsey, H. Rushmeier, "Understanding and Improving the Realism of Image Composites", ACM
Transactions on Graphics (SIGGRAPH'12), vol. 31, no. 4, 2012.
• [Tsai+17] Y. H. Tsai, X. Shen, Z. Lin, K. Sunkavalli, X. Lu, M. H. Yang, Deep Image Harmonization, In Proceedings of the IEEE
Conference on Computer Vision and Pattern Recognition (CVPR'17), pp. 2799-2807, 2017.
• [Cong+19] W. Cong, J. Zhang, L. Niu, L. Liu, Z. Ling, W. Li, L. Zhang, "DoveNet: Deep Image Harmonization via Domain
Verification", arXiv preprint arXiv:1911.13239, 2019.
• [Zhang+19] L. Zhang, T. Wen, J. Shi, "Deep Image Blending", arXiv preprint arXiv:1910.11495, 2019.
• [Gatys+16] L. A. Gatys, A. S. Ecker, M. Bethge, "Image style transfer using convolutional neural networks", In Proceedings of the
IEEE Conference on Computer Vision and Pattern Recognition (CVPR'16), pages 2414–2423, 2016.
• [Lin+19] C. H. Lin, E. Yumer, O. Wang, E. Shechtman, S. Lucey, "ST-GAN: Spatial Transformer Generative Adversarial Networks
• for Image Compositing", In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR'19), pp. 9455-
9464, 2019.
• [Jaderberg+15] M. Jaderberg, K. Simonyan, A. Zisserman, K. Kavukcuoglu, "Spatial Transformer Networks", In Proceedings of the
28th International Conference on Neural Information Processing Systems (NIPS'15), vol. 2, pp. 2017–2025, 2015.
• [Zhan+19] F. Zhan, H. Zhu, S. Lu, "Spatial Fusion GAN for Image Synthesis", In Proceedings of the IEEE Conference on Computer
Vision and Pattern Recognition (CVPR'19), pp. 3648-3657, 2019.
36