Identity-Preserving Face Recovery from Stylized Portraits
暂无分享,去创建一个
[1] Xin Yu,et al. Recovering Faces From Portraits with Auxiliary Facial Attributes , 2019, 2019 IEEE Winter Conference on Applications of Computer Vision (WACV).
[2] Rui Zhang,et al. Museum Exhibit Identification Challenge for the Supervised Domain Adaptation and Beyond , 2018, ECCV.
[3] Fatih Murat Porikli,et al. A Deeper Look at Power Normalizations , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[4] Nasser M. Nasrabadi,et al. Facial Attributes Guided Deep Sketch-to-Photo Synthesis , 2018, 2018 IEEE Winter Applications of Computer Vision Workshops (WACVW).
[5] Piotr Koniusz,et al. Identity-Preserving Face Recovery from Portraits , 2018, 2018 IEEE Winter Conference on Applications of Computer Vision (WACV).
[6] Xin Yu,et al. Face Destylization , 2017, 2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA).
[7] Vishal M. Patel,et al. High-Quality Facial Photo-Sketch Synthesis Using Multi-Adversarial Networks , 2017, 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018).
[8] Xin Yu,et al. Hallucinating Very Low-Resolution Unaligned and Noisy Face Images by Transformative Discriminative Autoencoders , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[9] Xinbo Gao,et al. Back projection: An effective postprocessing method for GAN-based face sketch synthesis , 2017, Pattern Recognit. Lett..
[10] Li Fei-Fei,et al. Characterizing and Improving Stability in Neural Style Transfer , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[11] Ran He,et al. Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[12] Nenghai Yu,et al. StyleBank: An Explicit Representation for Neural Image Style Transfer , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[13] Hang Zhang,et al. Multi-style Generative Network for Real-time Transfer , 2017, ECCV Workshops.
[14] Serge J. Belongie,et al. Arbitrary Style Transfer in Real-Time with Adaptive Instance Normalization , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[15] Ming-Hsuan Yang,et al. Diversified Texture Synthesis with Feed-Forward Networks , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[16] Xin Yu,et al. Face Hallucination with Tiny Unaligned Images by Transformative Discriminative Neural Networks , 2017, AAAI.
[17] Connelly Barnes,et al. Stable and Controllable Neural Texture Synthesis and Style Transfer Using Histogram Losses , 2017, ArXiv.
[18] Vishal M. Patel,et al. Image De-Raining Using a Conditional Generative Adversarial Network , 2017, IEEE Transactions on Circuits and Systems for Video Technology.
[19] Andrea Vedaldi,et al. Improved Texture Networks: Maximizing Quality and Diversity in Feed-Forward Stylization and Texture Synthesis , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[20] Mark W. Schmidt,et al. Fast Patch-based Style Transfer of Arbitrary Style , 2016, ArXiv.
[21] Fisher Yu,et al. Scribbler: Controlling Deep Image Synthesis with Sketch and Color , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[22] Aykut Erdem,et al. Learning to Generate Images of Outdoor Scenes from Attributes and Semantic Layouts , 2016, ArXiv.
[23] Leon A. Gatys,et al. Controlling Perceptual Factors in Neural Style Transfer , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[24] Alexei A. Efros,et al. Image-to-Image Translation with Conditional Adversarial Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[25] Xin Wang,et al. Multimodal Transfer: A Hierarchical Deep Convolutional Neural Network for Fast Artistic Style Transfer , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[26] Jonathon Shlens,et al. A Learned Representation For Artistic Style , 2016, ICLR.
[27] Xin Yu,et al. Ultra-Resolving Face Images by Discriminative Generative Networks , 2016, ECCV.
[28] Christian Ledig,et al. Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[29] Andrea Vedaldi,et al. Instance Normalization: The Missing Ingredient for Fast Stylization , 2016, ArXiv.
[30] Linda Doyle,et al. Painting style transfer for head portraits using convolutional neural networks , 2016, ACM Trans. Graph..
[31] Leon A. Gatys,et al. Image Style Transfer Using Convolutional Neural Networks , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[32] Leon A. Gatys,et al. Preserving Color in Neural Artistic Style Transfer , 2016, ArXiv.
[33] Alexei A. Efros,et al. Context Encoders: Feature Learning by Inpainting , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[34] Chuan Li,et al. Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks , 2016, ECCV.
[35] Andrea Vedaldi,et al. Texture Networks: Feed-forward Synthesis of Textures and Stylized Images , 2016, ICML.
[36] Chuan Li,et al. Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[37] Rujie Yin,et al. Content Aware Neural Style Transfer , 2016, ArXiv.
[38] Soumith Chintala,et al. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.
[39] Rob Fergus,et al. Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks , 2015, NIPS.
[40] Yinda Zhang,et al. LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop , 2015, ArXiv.
[41] Andrew Zisserman,et al. Spatial Transformer Networks , 2015, NIPS.
[42] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.
[43] James Philbin,et al. FaceNet: A unified embedding for face recognition and clustering , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[44] Xiaogang Wang,et al. Deep Learning Face Attributes in the Wild , 2014, 2015 IEEE International Conference on Computer Vision (ICCV).
[45] Trevor Darrell,et al. Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[46] Xiaoou Tang,et al. Facial Landmark Detection by Deep Multi-task Learning , 2014, ECCV.
[47] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[48] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[49] Hongdong Li,et al. Kernel Methods on the Riemannian Manifold of Symmetric Positive Definite Matrices , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[50] David Zhang,et al. FSIM: A Feature Similarity Index for Image Quality Assessment , 2011, IEEE Transactions on Image Processing.
[51] D. Jacobs,et al. Bypassing synthesis: PLS for face recognition with pose, low-resolution and sketch , 2011, CVPR 2011.
[52] Xiaogang Wang,et al. Coupled information-theoretic encoding for face photo-sketch recognition , 2011, CVPR 2011.
[53] Terence Sim,et al. A study on recognizing non-artistic face sketches , 2011, 2011 IEEE Workshop on Applications of Computer Vision (WACV).
[54] Eero P. Simoncelli,et al. Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.
[55] Harry Wechsler,et al. The FERET database and evaluation procedure for face-recognition algorithms , 1998, Image Vis. Comput..
[56] K. Mikolajczyk,et al. Higher-Order Occurrence Pooling for Bags-of-Words: Visual Concept Detection. , 2017, IEEE transactions on pattern analysis and machine intelligence.
[57] Andrew Zisserman,et al. Deep Face Recognition , 2015, BMVC.
[58] Andrew L. Maas. Rectifier Nonlinearities Improve Neural Network Acoustic Models , 2013 .