42 citations · 129 across the 10 of their papers we have counts for
13 papers
Cross-Domain Similarity Learning for Face Recognition in Unseen Domains
Masoud Faraki, Xiang Yu, Yi-Hsuan Tsai +2
Face recognition models trained under the assumption of identical training and test distributions often suffer from poor generalization when faced with unknown variations, such as…
Improving Face Recognition by Clustering Unlabeled Faces in the Wild
Aruni RoyChowdhury, Xiang Yu, Kihyuk Sohn +2
While deep face recognition has benefited significantly from large-scale labeled data, current research is focused on leveraging unlabeled data to further boost performance, reduci…
Towards Universal Representation Learning for Deep Face Recognition
Yichun Shi, Xiang Yu, Kihyuk Sohn +2
Recognizing wild faces is extremely hard as they appear with all kinds of variations. Traditional methods either train with specifically annotated variation data from target domain…
DAVID: Dual-Attentional Video Deblurring
Junru Wu, Xiang Yu, Ding Liu +2
Blind video deblurring restores sharp frames from a blurry sequence without any prior. It is a challenging task because the blur due to camera shake, object movement and defocusing…
Pose-variant 3D Facial Attribute Generation
Feng-Ju Chang, Xiang Yu, Ram Nevatia +1
We address the challenging problem of generating facial attributes using a single image in an unconstrained pose. In contrast to prior works that largely consider generation on 2D…
Feature Transfer Learning for Deep Face Recognition with Under-Represented Data
Xi Yin, Xiang Yu, Kihyuk Sohn +2
Despite the large volume of face recognition datasets, there is a significant portion of subjects, of which the samples are insufficient and thus under-represented. Ignoring such s…
Gotta Adapt 'Em All: Joint Pixel and Feature-Level Domain Adaptation for Recognition in the Wild
Luan Tran, Kihyuk Sohn, Xiang Yu +2
Recent developments in deep domain adaptation have allowed knowledge transfer from a labeled source domain to an unlabeled target domain at the level of intermediate features or in…
Deep Supervision with Intermediate Concepts
Chi Li, M. Zeeshan Zia, Quoc-Huy Tran +3
Recent data-driven approaches to scene interpretation predominantly pose inference as an end-to-end black-box mapping, commonly performed by a Convolutional Neural Network (CNN). H…
Unsupervised Domain Adaptation for Face Recognition in Unlabeled Videos
Kihyuk Sohn, Sifei Liu, Guangyu Zhong +3
Despite rapid advances in face recognition, there remains a clear gap between the performance of still image-based face recognition and video-based face recognition, due to the vas…
Towards Large-Pose Face Frontalization in the Wild
Xi Yin, Xiang Yu, Kihyuk Sohn +2
Despite recent advances in face recognition using deep learning, severe accuracy drops are observed for large pose variations in unconstrained environments. Learning pose-invariant…
Reconstruction-Based Disentanglement for Pose-invariant Face Recognition
Xi Peng, Xiang Yu, Kihyuk Sohn +2
Deep neural networks (DNNs) trained on large-scale datasets have recently achieved impressive improvements in face recognition. But a persistent challenge remains to develop method…
Deep Supervision with Shape Concepts for Occlusion-Aware 3D Object Parsing
Chi Li, M. Zeeshan Zia, Quoc-Huy Tran +3
Monocular 3D object parsing is highly desirable in various scenarios including occlusion reasoning and holistic scene interpretation. We present a deep convolutional neural network…
Deep Deformation Network for Object Landmark Localization
Xiang Yu, Feng Zhou, Manmohan Chandraker
We propose a novel cascaded framework, namely deep deformation network (DDN), for localizing landmarks in non-rigid objects. The hallmarks of DDN are its incorporation of geometric…