标签：ICCV
AAVAE: Augmentation-Augmented Variational Autoencoders
AAVAE: Augmentation-Augmented Variational Autoencoders William Falcon 1,2&Ananya Harsh Jha 1*&Teddy Koker 1&Kyunghyun Cho 2,31 Grid AI Labs2 New York University3 CIFAR……
H3D-Net: Few-Shot High-Fidelity 3D Head Reconstruction
H3D-Net: Few-Shot High-Fidelity 3D Head Reconstruction Eduard Ramon1,2 Gil Triginer1 Janna Escur1 Albert Pumarola3 Jaime Garcia1 &e……
Language Grounding with 3D Objects
Language Grounding with 3D Objects Jesse ThomasonUniversity of Southern California &Mohit Shridhar∗University of Washington/ANDYonatan BiskCarnegie Mellon University &……
CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows
CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localizationvia Conditional Normalizing Flows Denis Gudovskiy1 Shun Ishizaka2 ……
A Physiologically-adapted Gold Standard for Arousal During a Stress Induced Scenario
A Physiologically-adapted Gold Standard for ArousalDuring a Stress Induced Scenario Alice BairdChair EIHW, University of AugsburgAugsburg, Germany, Lukas StappenChair EIHW, ……
Contextual Transformer Networks for Visual Recognition
C++ontextual Transformer Networks for Visual Recognition Yehao Li, Ting Yao, Yingwei Pan, and Tao MeiJD AI Research, Beijing, China{yehaoli.sysu, tingyao.ustc, , Abstract Tran……
RankSRGAN: Super Resolution Generative Adversarial Networks with Learning to Rank
RankSRGAN: Super Resolution Generative Adversarial Networks with Learning to Rank Wenlong Zhang, Yihao Liu, Chao Dong, Yu QiaoWenlong Zhang, Yihao Liu, Chao Dong and Yu Qiao are w……
Anomaly Detection via Self-organizing Map
Anomaly Detection via Self-organizing Map Abstract Anomaly detection plays a key role in industrial manufacturing for product quality control. Traditional methods for anomaly dete……
ROMA: Free Lunch via Random Mappings for Unsupervised Visual Representation LearningTriplet is All You Need with Random Mappings for Unsupervised Visual Representation Learning
ROMA: Free Lunch via Random Mappings for Unsupervised Visual Representation Learning Wenbin Li1, Xuesong Yang1, Meihao Kong 1, Lei Wang2, Jing Huo1, Yang Gao1, Jiebo Luo31Nanjing ……
Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data
Real-ESRGAN: Training Real-World Blind Super-Resolutionwith Pure Synthetic Data Xintao Wang1 Liangbin Xie∗2,3 Chao Dong2 ……
