• Opto-Electronic Advances
  • Vol. 6, Issue 2, 220049 (2023)
Yangyundou Wang1、2、*, Hao Wang3, and Min Gu1、2、**
Author Affiliations
  • 1Institute of Photonic Chips, University of Shanghai for Science and Technology, Shanghai 200093, China
  • 2Centre for Artificial-Intelligence Nanophotonics, School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
  • 3School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
  • show less
    DOI: 10.29026/oea.2023.220049 Cite this Article
    Yangyundou Wang, Hao Wang, Min Gu. High performance “non-local” generic face reconstruction model using the lightweight Speckle-Transformer (SpT) UNet[J]. Opto-Electronic Advances, 2023, 6(2): 220049 Copy Citation Text show less
    References

    [1] Goodman JW. Speckle Phenomena in Optics: Theory and Applications (Roberts and Company Publishers, Englewood, 2007).

    [15] Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L et al. Attention is all you need. In Proceedings of the 31st International Conference on Neural Information Processing Systems 6000–6010 (ACM, 2017).

    [17] Lin TY, Wang YX, Liu XY, Qiu XP. A survey of transformers. (2021); https://arxiv.org/abs/2106.04554.

    [18] Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai XH et al. An image is worth 16x16 words: transformers for image recognition at scale. In Proceedings of the 9th International Conference on Learning Representations (ICLR, 2020).

    [19] Touvron H, Cord M, Douze M, Massa F, Sablayrolles A et al. Training data-efficient image transformers & distillation through attention. In Proceedings of the 38th International Conference on Machine Learning 10347–10357 (PMLR, 2021).

    [20] Ye LW, Rochan M, Liu Z, Wang Y. Cross-modal self-attention network for referring image segmentation. In Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition 10494–10503 (IEEE, 2019).

    [21] Yang FZ, Yang H, Fu JL, Lu HT, Guo BN. Learning texture transformer network for image super-resolution. In Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition 5790–5799 (IEEE, 2020).

    [22] Sun C, Myers A, Vondrick C, Murphy K, Schmid C. Videobert: a joint model for video and language representation learning. In Proceedings of 2019 IEEE/CVF International Conference on Computer Vision 7463–7472 (IEEE, 2019).

    [23] Girdhar R, Carreira JJ, Doersch C, Zisserman A. Video action transformer network. In Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition 244–253 (IEEE, 2021).

    [24] Chen HT, Wang YH, Guo TY, Xu C, Deng YP et al. Pre-trained image processing transformer. In Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition 12294–12305 (IEEE, 2021);http://doi.org/10.1109/CVPR46437.2021.01212.

    [25] Ramesh A, Pavlov M, Goh G, Gray S, Voss C et al. Zero-shot text-to-image generation. In Proceedings of the 38th International Conference on Machine Learning 8821–8831 (PMLR, 2021).

    [26] Khan S, Naseer M, Hayat M, Zamir SW, Khan FS et al. Transformers in vision: a survey. (2021);https://arxiv.org/abs/2101.01169.

    [27] Liu Z, Lin YT, Cao Y, Hu H, Wei YX et al. Swin transformer: hierarchical vision transformer using shifted windows. In Proceedings of 2021 IEEE/CVF International Conference on Computer Vision 9992–10002 (IEEE, 2021).

    [28] He KM, Zhang XY, Ren SQ, Sun J. Deep residual learning for image recognition. In Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition 770–778 (IEEE, 2016); http://doi.org/10.1109/CVPR.2016.90.

    [29] Huang GB, Mattar M, Berg T, Learned-Miller E. Labeled faces in the wild: a database forstudying face recognition in unconstrained environments. In Proceedings of Workshop on Faces in ‘Real-Life’ Images: Detection, Alignment, and Recognition (HAL, 2008).

    Yangyundou Wang, Hao Wang, Min Gu. High performance “non-local” generic face reconstruction model using the lightweight Speckle-Transformer (SpT) UNet[J]. Opto-Electronic Advances, 2023, 6(2): 220049
    Download Citation