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Facial UV map completion for pose-invariant face recognition: a novel adversarial approach based on coupled attention residual UNets

机译：面部紫外线地图完成姿势不变性面貌识别：一种基于耦合注意力剩余缺陷的新型对抗方法

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In Seop Na; Chung Tran; Dung Nguyen; Sang Dinh;
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1. Coupled Gaussian Processes for Pose-Invariant Facial Expression Recognition [J] . Rudovic Ognjen, Pantic Maja, Patras Ioannis (Yiannis) Pattern Analysis and Machine Intelligence, IEEE Transactions on . 2013,第6期

机译：耦合高斯过程用于姿势不变的面部表情识别
2. Multi-Subregion-Based Probabilistic Approach to Pose-Invariant Face Recognition [J] . Akihiko Yamada, Takeo Kanade Systems and Computers in Japan . 2006,第8期

机译：基于多分区的姿态不变人脸识别的概率方法
3. Fine-grained facial image-to-image translation with an attention based pipeline generative adversarial framework [J] . Yan Zhao, Ziqiang Zheng, Chao Wang, Multimedia Tools and Applications . 2020,第21a22期

机译：用基于Pirce Dental对抗框架的注意力细粒面部图像到图像转换
4. UV-GAN: Adversarial Facial UV Map Completion for Pose-Invariant Face Recognition [C] . Jiankang Deng, Shiyang Cheng, Niannan Xue, IEEE/CVF Conference on Computer Vision and Pattern Recognition . 2018

机译：UV-GAN：对抗性面部UV贴图完成，用于姿势不变的面部识别
5. Two Approaches to Defend Against Adversarial Examples: Attention-Based and Certificate-Based [D] . Nguyen, Chanh 2018

机译：对抗对抗示例的两种方法：基于注意和基于证书
6. CT-Based Pelvic T1-Weighted MR Image Synthesis Using UNet UNet++ and Cycle-Consistent Generative Adversarial Network (Cycle-GAN) [O] . Reza Kalantar, Christina Messiou, Jessica M. Winfield, 2021

机译：基于CT的骨盆T1加权MR图像合成使用UNETUNET ++和循环一致的生成对抗网络（CYCLE-GAN）
7. Coupled Gaussian processes for pose-invariant facial expression recognition [O] . Rudovic, Ognjen, Pantic, Maja, Patras, Ioannis (Yannis) 2013

机译：耦合高斯过程用于姿势不变的面部表情识别
8. Integrated Color CCD and LADAR Based Object Modeling and Recognition: ACoregistration Approach to Multisensor Target Recognition with Extensions to Exploit Digital Elevation Map Data [R] . Beveridge, J. R., Draper, B. A., Stevens, M. R., 1997

机译：基于集成彩色CCD和LaDaR的对象建模与识别：具有扩展的多传感器目标识别的交互注册方法，用于利用数字高程地图数据

Facial UV map completion for pose-invariant face recognition: a novel adversarial approach based on coupled attention residual UNets

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