activity
20172021
most citedDeep Heterogeneous Feature Fusion for Template-Based Face Recognition

12 citations · 12 across the 2 of their papers we have counts for

collaborators

6 papers

cs.CV2021

Multi-modal 3D Human Pose Estimation with 2D Weak Supervision in Autonomous Driving

Jingxiao Zheng, Xinwei Shi, Alexander Gorban +9

3D human pose estimation (HPE) in autonomous vehicles (AV) differs from other use cases in many factors, including the 3D resolution and range of data, absence of dense depth maps,…

cs.CV2019

Uncertainty Modeling of Contextual-Connections between Tracklets for Unconstrained Video-based Face Recognition

Jingxiao Zheng, Ruichi Yu, Jun-Cheng Chen +3

Unconstrained video-based face recognition is a challenging problem due to significant within-video variations caused by pose, occlusion and blur. To tackle this problem, an effect…

cs.CV2018

An Automatic System for Unconstrained Video-Based Face Recognition

Jingxiao Zheng, Rajeev Ranjan, Ching-Hui Chen +3

Although deep learning approaches have achieved performance surpassing humans for still image-based face recognition, unconstrained video-based face recognition is still a challeng…

cs.CV2018

A Fast and Accurate System for Face Detection, Identification, and Verification

Rajeev Ranjan, Ankan Bansal, Jingxiao Zheng +7

The availability of large annotated datasets and affordable computation power have led to impressive improvements in the performance of CNNs on various object detection and recogni…

cs.CV2018

Layout-induced Video Representation for Recognizing Agent-in-Place Actions

Ruichi Yu, Hongcheng Wang, Ang Li +3

We address the recognition of agent-in-place actions, which are associated with agents who perform them and places where they occur, in the context of outdoor home surveillance. We…

cs.CV2017★ 12 cited

Deep Heterogeneous Feature Fusion for Template-Based Face Recognition

Navaneeth Bodla, Jingxiao Zheng, Hongyu Xu +3

Although deep learning has yielded impressive performance for face recognition, many studies have shown that different networks learn different feature maps: while some networks ar…