102 citations · 215 across the 22 of their papers we have counts for
22 papers
Heterogeneous Multi-Task Gaussian Cox Processes
Feng Zhou, Quyu Kong, Zhijie Deng +3
This paper presents a novel extension of multi-task Gaussian Cox processes for modeling multiple heterogeneous correlated tasks jointly, e.g., classification and regression, via mu…
Navigating in the Dark -- Designing Autonomous Driving Features to Assist Old Adults with Visual Impairments
Lashawnda Bynum, Jay Parker, Kristy Lee +7
Age-related macular degeneration is a leading cause of blindness worldwide and is one of many limitations to independent driving among old adults. Highly autonomous vehicles presen…
Improving Crowded Object Detection via Copy-Paste
Jiangfan Deng, Dewen Fan, Xiaosong Qiu +1
Crowdedness caused by overlapping among similar objects is a ubiquitous challenge in the field of 2D visual object detection. In this paper, we first underline two main effects of…
Accelerated Linearized Laplace Approximation for Bayesian Deep Learning
Zhijie Deng, Feng Zhou, Jun Zhu
Laplace approximation (LA) and its linearized variant (LLA) enable effortless adaptation of pretrained deep neural networks to Bayesian neural networks. The generalized Gauss-Newto…
Towards Privacy-Preserving, Real-Time and Lossless Feature Matching
Qiang Meng, Feng Zhou
Most visual retrieval applications store feature vectors for downstream matching tasks. These vectors, from where user information can be spied out, will cause privacy leakage if n…
Investigating Explanations in Conditional and Highly Automated Driving: The Effects of Situation Awareness and Modality
Lilit Avetisyan, Jackie Ayoub, Feng Zhou
With the level of automation increases in vehicles, such as conditional and highly automated vehicles (AVs), drivers are becoming increasingly out of the control loop, especially i…
Deep Ensemble as a Gaussian Process Approximate Posterior
Zhijie Deng, Feng Zhou, Jianfei Chen +2
Deep Ensemble (DE) is an effective alternative to Bayesian neural networks for uncertainty quantification in deep learning. The uncertainty of DE is usually conveyed by the functio…
Spatial-Temporal-Fusion BNN: Variational Bayesian Feature Layer
Shiye Lei, Zhuozhuo Tu, Leszek Rutkowski +4
Bayesian neural networks (BNNs) have become a principal approach to alleviate overconfident predictions in deep learning, but they often suffer from scaling issues due to a large n…
PoseFace: Pose-Invariant Features and Pose-Adaptive Loss for Face Recognition
Qiang Meng, Xiaqing Xu, Xiaobo Wang +6
Despite the great success achieved by deep learning methods in face recognition, severe performance drops are observed for large pose variations in unconstrained environments (e.g.…
Bias-Tolerant Fair Classification
Yixuan Zhang, Feng Zhou, Zhidong Li +2
The label bias and selection bias are acknowledged as two reasons in data that will hinder the fairness of machine-learning outcomes. The label bias occurs when the labeling decisi…
Nonlinear Hawkes Processes in Time-Varying System
Feng Zhou, Quyu Kong, Yixuan Zhang +2
Hawkes processes are a class of point processes that have the ability to model the self- and mutual-exciting phenomena. Although the classic Hawkes processes cover a wide range of…
Unsupervised Classification for Polarimetric SAR Data Using Variational Bayesian Wishart Mixture Model with Inverse Gamma-Gamma Prior
Shijie Ren, Feng Zhou, Changlong Wang
Although various clustering methods have been successfully applied to polarimetric synthetic aperture radar (PolSAR) image clustering tasks, most of the available approaches fail t…
Using Eye-tracking Data to Predict Situation Awareness in Real Time during Takeover Transitions in Conditionally Automated Driving
Feng Zhou, X. Jessie Yang, Joost de Winter
Situation awareness (SA) is critical to improving takeover performance during the transition period from automated driving to manual driving. Although many studies measured SA duri…
Searching for Alignment in Face Recognition
Xiaqing Xu, Qiang Meng, Yunxiao Qin +4
A standard pipeline of current face recognition frameworks consists of four individual steps: locating a face with a rough bounding box and several fiducial landmarks, aligning the…
Visible Feature Guidance for Crowd Pedestrian Detection
Zhida Huang, Kaiyu Yue, Jiangfan Deng +1
Heavy occlusion and dense gathering in crowd scene make pedestrian detection become a challenging problem, because it's difficult to guess a precise full bounding box according to…
Matching Guided Distillation
Kaiyu Yue, Jiangfan Deng, Feng Zhou
Feature distillation is an effective way to improve the performance for a smaller student model, which has fewer parameters and lower computation cost compared to the larger teache…
Efficient Inference of Flexible Interaction in Spiking-neuron Networks
Feng Zhou, Yixuan Zhang, Jun Zhu
Hawkes process provides an effective statistical framework for analyzing the time-dependent interaction of neuronal spiking activities. Although utilized in many real applications,…
Deep Spatial Gradient and Temporal Depth Learning for Face Anti-spoofing
Zezheng Wang, Zitong Yu, Chenxu Zhao +5
Face anti-spoofing is critical to the security of face recognition systems. Depth supervised learning has been proven as one of the most effective methods for face anti-spoofing. D…
Searching Central Difference Convolutional Networks for Face Anti-Spoofing
Zitong Yu, Chenxu Zhao, Zezheng Wang +5
Face anti-spoofing (FAS) plays a vital role in face recognition systems. Most state-of-the-art FAS methods 1) rely on stacked convolutions and expert-designed network, which is wea…
Deep Variational Luenberger-type Observer for Stochastic Video Prediction
Dong Wang, Feng Zhou, Zheng Yan +4
Considering the inherent stochasticity and uncertainty, predicting future video frames is exceptionally challenging. In this work, we study the problem of video prediction by combi…
Scalable Inference for Nonparametric Hawkes Process Using Pólya-Gamma Augmentation
Feng Zhou, Zhidong Li, Xuhui Fan +3
In this paper, we consider the sigmoid Gaussian Hawkes process model: the baseline intensity and triggering kernel of Hawkes process are both modeled as the sigmoid transformation…
Learning Meta Model for Zero- and Few-shot Face Anti-spoofing
Yunxiao Qin, Chenxu Zhao, Xiangyu Zhu +6
Face anti-spoofing is crucial to the security of face recognition systems. Most previous methods formulate face anti-spoofing as a supervised learning problem to detect various pre…