b'Feng Zhou'

research

∙ 09/09/2023

RRCNN^+: An Enhanced Residual Recursive Convolutional Neural Network for Non-stationary Signal Decomposition

Time-frequency analysis is an important and challenging task in many app...

0 Feng Zhou, et al. ∙

research

∙ 09/08/2023

From Text to Mask: Localizing Entities Using the Attention of Text-to-Image Diffusion Models

Diffusion models have revolted the field of text-to-image generation rec...

0 Changming Xiao, et al. ∙

research

∙ 08/30/2023

Zero-shot Inversion Process for Image Attribute Editing with Diffusion Models

Denoising diffusion models have shown outstanding performance in image e...

0 Zhanbo Feng, et al. ∙

research

∙ 08/29/2023

Heterogeneous Multi-Task Gaussian Cox Processes

This paper presents a novel extension of multi-task Gaussian Cox process...

0 Feng Zhou, et al. ∙

research

∙ 08/17/2023

Enhancing Phrase Representation by Information Bottleneck Guided Text Diffusion Process for Keyphrase Extraction

Keyphrase extraction (KPE) is an important task in Natural Language Proc...

0 Yuanzhen Luo, et al. ∙

research

∙ 07/18/2023

On the (In)Effectiveness of Large Language Models for Chinese Text Correction

Recently, the development and progress of Large Language Models (LLMs) h...

0 Yinghui Li, et al. ∙

research

∙ 07/04/2023

RRCNN: A novel signal decomposition approach based on recurrent residue convolutional neural network

The decomposition of non-stationary signals is an important and challeng...

0 Feng Zhou, et al. ∙

research

∙ 06/28/2023

Building Trust Profiles in Conditionally Automated Driving

Trust is crucial for ensuring the safety, security, and widespread adopt...

0 Lilit Avetisyan, et al. ∙

research

∙ 05/28/2023

Investigating HMIs to Foster Communications between Conventional Vehicles and Autonomous Vehicles in Intersections

In mixed traffic environments that involve conventional vehicles (CVs) a...

0 Lilit Avetisyan, et al. ∙

research

∙ 05/18/2023

A systematic review of safety-critical scenarios between automated vehicles and vulnerable road users

Automated vehicles (AVs) are of great potential in reducing crashes on t...

0 Aditya Deshmukh, et al. ∙

research

∙ 03/13/2023

An Improved Baseline Framework for Pose Estimation Challenge at ECCV 2022 Visual Perception for Navigation in Human Environments Workshop

This technical report describes our first-place solution to the pose est...

0 Jiajun Fu, et al. ∙

research

∙ 02/01/2023

Navigating in the Dark – Designing Autonomous Driving Features to Assist Old Adults with Visual Impairments

Age-related macular degeneration is a leading cause of blindness worldwi...

0 Lashawnda Bynum, et al. ∙

research

∙ 12/01/2022

Real-time Trust Prediction in Conditionally Automated Driving Using Physiological Measures

Trust calibration presents a main challenge during the interaction betwe...

0 Jackie Ayoub, et al. ∙

research

∙ 11/22/2022

Improving Crowded Object Detection via Copy-Paste

Crowdedness caused by overlapping among similar objects is a ubiquitous ...

0 Jiangfan Deng, et al. ∙

research

∙ 11/05/2022

Cross-Subject Emotion Recognition with Sparsely-Labeled Peripheral Physiological Data Using SHAP-Explained Tree Ensembles

There are still many challenges of emotion recognition using physiologic...

0 Feng Zhou, et al. ∙

research

∙ 10/25/2022

Information Filter upon Diversity-Improved Decoding for Diversity-Faithfulness Tradeoff in NLG

Some Natural Language Generation (NLG) tasks require both faithfulness a...

0 Han Meng, et al. ∙

research

∙ 10/23/2022

Accelerated Linearized Laplace Approximation for Bayesian Deep Learning

Laplace approximation (LA) and its linearized variant (LLA) enable effor...

0 Zhijie Deng, et al. ∙

research

∙ 10/21/2022

Design a Sustainable Micro-mobility Future: Trends and Challenges in the United States and European Union Using Natural Language Processing Techniques

Micro-mobility is promising to contribute to sustainable cities in the f...

0 Lilit Avetisyan, et al. ∙

research

∙ 08/01/2022

De-biased Representation Learning for Fairness with Unreliable Labels

Removing bias while keeping all task-relevant information is challenging...

0 Yixuan Zhang, et al. ∙

research

∙ 07/30/2022

Cause-and-Effect Analysis of ADAS: A Comparison Study between Literature Review and Complaint Data

Advanced driver assistance systems (ADAS) are designed to improve vehicl...

0 Jackie Ayoub, et al. ∙

research

∙ 07/30/2022

Towards Privacy-Preserving, Real-Time and Lossless Feature Matching

Most visual retrieval applications store feature vectors for downstream ...

0 Qiang Meng, et al. ∙

research

∙ 07/15/2022

Investigating Explanations in Conditional and Highly Automated Driving: The Effects of Situation Awareness and Modality

With the level of automation increases in vehicles, such as conditional ...

0 Lilit Avetisyan, et al. ∙

research

∙ 06/18/2022

Anticipated emotions associated with trust in autonomous vehicles

Trust in automation has been mainly studied in the cognitive perspective...

0 Lilit Avetisian, et al. ∙

research

∙ 04/30/2022

Deep Ensemble as a Gaussian Process Approximate Posterior

Deep Ensemble (DE) is an effective alternative to Bayesian neural networ...

0 Zhijie Deng, et al. ∙

research

∙ 01/29/2022

Improving Federated Learning Face Recognition via Privacy-Agnostic Clusters

The growing public concerns on data privacy in face recognition can be g...

0 Qiang Meng, et al. ∙

research

∙ 01/23/2022

Basket-based Softmax

Softmax-based losses have achieved state-of-the-art performances on vari...

0 Qiang Meng, et al. ∙

research

∙ 12/12/2021

Spatial-Temporal-Fusion BNN: Variational Bayesian Feature Layer

Bayesian neural networks (BNNs) have become a principal approach to alle...

0 Shiye Lei, et al. ∙

research

∙ 12/08/2021

An Investigation of Drivers' Dynamic Situational Trust in Conditionally Automated Driving

Understanding how trust is built over time is essential, as trust plays ...

0 Jackie Ayoub, et al. ∙

research

∙ 11/05/2021

Disengagement Cause-and-Effect Relationships Extraction Using an NLP Pipeline

The advancement in machine learning and artificial intelligence is promo...

0 Yangtao Zhang, et al. ∙

research

∙ 08/09/2021

An Autonomous Driving System - Dedicated Vehicle for People with ASD and their Caregivers

Automated driving system - dedicated vehicles (ADS-DVs), specially desig...

0 Gandhimathi Padmanaban, et al. ∙

research

∙ 08/04/2021

Learning Compatible Embeddings

Achieving backward compatibility when rolling out new models can highly ...

0 Qiang Meng, et al. ∙

research

∙ 07/25/2021

PoseFace: Pose-Invariant Features and Pose-Adaptive Loss for Face Recognition

Despite the great success achieved by deep learning methods in face reco...

9 Qiang Meng, et al. ∙

research

∙ 07/21/2021

Investigating External Interaction Modality and Design Between Automated Vehicles and Pedestrians at Crossings

In this study, we investigated the effectiveness and user acceptance of ...

0 Sue Bai, et al. ∙

research

∙ 07/20/2021

Predicting Driver Takeover Time in Conditionally Automated Driving

It is extremely important to ensure a safe takeover transition in condit...

0 Jackie Ayoub, et al. ∙

research

∙ 07/07/2021

Bias-Tolerant Fair Classification

The label bias and selection bias are acknowledged as two reasons in dat...

0 Yixuan Zhang, et al. ∙

research

∙ 06/09/2021

Nonlinear Hawkes Processes in Time-Varying System

Hawkes processes are a class of point processes that have the ability to...

0 Feng Zhou, et al. ∙

research

∙ 03/27/2021

Using Eye-tracking Data to Predict Situation Awareness in Real Time during Takeover Transitions in Conditionally Automated Driving

Situation awareness (SA) is critical to improving takeover performance d...

0 Feng Zhou, et al. ∙

research

∙ 03/11/2021

MagFace: A Universal Representation for Face Recognition and Quality Assessment

The performance of face recognition system degrades when the variability...

27 Qiang Meng, et al. ∙

research

∙ 03/03/2021

Predicting Driver Fatigue in Automated Driving with Explainability

Research indicates that monotonous automated driving increases the incid...

0 Feng Zhou, et al. ∙

research

∙ 03/01/2021

Combat COVID-19 Infodemic Using Explainable Natural Language Processing Models

Misinformation of COVID-19 is prevalent on social media as the pandemic ...

0 Jackie Ayoub, et al. ∙

research

∙ 02/10/2021

Searching for Alignment in Face Recognition

A standard pipeline of current face recognition frameworks consists of f...

0 Xiaqing Xu, et al. ∙

research

∙ 12/25/2020

Modeling Dispositional and Initial learned Trust in Automated Vehicles with Predictability and Explainability

Technological advances in the automotive industry are bringing automated...

0 Jackie Ayoub, et al. ∙

research

∙ 10/06/2020

Psychophysiological responses to takeover requests in conditionally automated driving

In SAE Level 3 automated driving, taking over control from automation ra...

0 Na Du, et al. ∙

research

∙ 10/06/2020

Emotional Design

Emotional design has been well recognized in the domain of human factors...

0 Feng Zhou, et al. ∙

research

∙ 09/21/2020

Physical-Layer Security for Two-Hop Air-to-Underwater Communication Systems With Fixed-Gain Amplify-and-Forward Relaying

We analyze a secure two-hop mixed radio frequency (RF) and underwater wi...

0 Yi Lou, et al. ∙

research

∙ 08/23/2020

Visible Feature Guidance for Crowd Pedestrian Detection

Heavy occlusion and dense gathering in crowd scene make pedestrian detec...

11 Zhida Huang, et al. ∙

research

∙ 08/23/2020

Matching Guided Distillation

Feature distillation is an effective way to improve the performance for ...

15 Kaiyu Yue, et al. ∙

research

∙ 06/30/2020

Do not forget interaction: Predicting fatality of COVID-19 patients using logistic regression

Amid the ongoing COVID-19 pandemic, whether COVID-19 patients with high ...

0 Feng Zhou, et al. ∙

research

∙ 06/23/2020

Efficient Inference of Nonparametric Interaction in Spiking-neuron Networks

Hawkes process provides an effective statistical framework for analyzing...

6 Feng Zhou, et al. ∙

research

∙ 06/16/2020

Additive Poisson Process: Learning Intensity of Higher-Order Interaction in Stochastic Processes

We present the Additive Poisson Process (APP), a novel framework that ca...

0 Simon Luo, et al. ∙

Feng Zhou

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