406 citations · 2.5k across the 53 of their papers we have counts for
10 papers · 1 filter
FSRNet: End-to-End Learning Face Super-Resolution with Facial Priors
Yu Chen, Ying Tai, Xiaoming Liu +2
Face Super-Resolution (SR) is a domain-specific super-resolution problem. The specific facial prior knowledge could be leveraged for better super-resolving face images. We present…
Do Convolutional Neural Networks Learn Class Hierarchy?
Bilal Alsallakh, Amin Jourabloo, Mao Ye +2
Convolutional Neural Networks (CNNs) currently achieve state-of-the-art accuracy in image classification. With a growing number of classes, the accuracy usually drops as the possib…
Dense Face Alignment
Yaojie Liu, Amin Jourabloo, William Ren +1
Face alignment is a classic problem in the computer vision field. Previous works mostly focus on sparse alignment with a limited number of facial landmark points, i.e., facial land…
MemNet: A Persistent Memory Network for Image Restoration
Ying Tai, Jian Yang, Xiaoming Liu +1
Recently, very deep convolutional neural networks (CNNs) have been attracting considerable attention in image restoration. However, as the depth grows, the long-term dependency pro…
Joint Face Alignment and 3D Face Reconstruction with Application to Face Recognition
Feng Liu, Qijun Zhao, Xiaoming Liu +1
Face alignment and 3D face reconstruction are traditionally accomplished as separated tasks. By exploring the strong correlation between 2D landmarks and 3D shapes, in contrast, we…
Pose-Invariant Face Alignment with a Single CNN
Amin Jourabloo, Mao Ye, Xiaoming Liu +1
Face alignment has witnessed substantial progress in the last decade. One of the recent focuses has been aligning a dense 3D face shape to face images with large head poses. The do…
Illuminating Pedestrians via Simultaneous Detection & Segmentation
Garrick Brazil, Xi Yin, Xiaoming Liu
Pedestrian detection is a critical problem in computer vision with significant impact on safety in urban autonomous driving. In this work, we explore how semantic segmentation can…
Representation Learning by Rotating Your Faces
Luan Tran, Xi Yin, Xiaoming Liu
The large pose discrepancy between two face images is one of the fundamental challenges in automatic face recognition. Conventional approaches to pose-invariant face recognition ei…
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…
Multi-Task Convolutional Neural Network for Pose-Invariant Face Recognition
Xi Yin, Xiaoming Liu
This paper explores multi-task learning (MTL) for face recognition. We answer the questions of how and why MTL can improve the face recognition performance. First, we propose a mul…