21 citations · 43 across the 8 of their papers we have counts for
13 papers
Generating the Modal Worker: A Cross-Model Audit of Race and Gender in LLM-Generated Personas Across 41 Occupations
Ilona van der Linden, Sahana Kumar, Arnav Dixit +4
As generative AI tools are increasingly used to portray people in professional roles, understanding their racial and gender representational biases is critical. We audit over 1.5 m…
The 9th AI City Challenge
Zheng Tang, Shuo Wang, David C. Anastasiu +25
The ninth AI City Challenge continues to advance real-world applications of computer vision and AI in transportation, industrial automation, and public safety. The 2025 edition fea…
PFformer: A Position-Free Transformer Variant for Extreme-Adaptive Multivariate Time Series Forecasting
Yanhong Li, David C. Anastasiu
Multivariate time series (MTS) forecasting is vital in fields like weather, energy, and finance. However, despite deep learning advancements, traditional Transformer-based models o…
WTS: A Pedestrian-Centric Traffic Video Dataset for Fine-grained Spatial-Temporal Understanding
Quan Kong, Yuki Kawana, Rajat Saini +8
In this paper, we address the challenge of fine-grained video event understanding in traffic scenarios, vital for autonomous driving and safety. Traditional datasets focus on drive…
The 8th AI City Challenge
Shuo Wang, David C. Anastasiu, Zheng Tang +21
The eighth AI City Challenge highlighted the convergence of computer vision and artificial intelligence in areas like retail, warehouse settings, and Intelligent Traffic Systems (I…
Learning from Polar Representation: An Extreme-Adaptive Model for Long-Term Time Series Forecasting
Yanhong Li, Jack Xu, David C. Anastasiu
In the hydrology field, time series forecasting is crucial for efficient water resource management, improving flood and drought control and increasing the safety and quality of lif…
The 7th AI City Challenge
Milind Naphade, Shuo Wang, David C. Anastasiu +17
The AI City Challenge's seventh edition emphasizes two domains at the intersection of computer vision and artificial intelligence - retail business and Intelligent Traffic Systems…
An Extreme-Adaptive Time Series Prediction Model Based on Probability-Enhanced LSTM Neural Networks
Yanhong Li, Jack Xu, David C. Anastasiu
Forecasting time series with extreme events has been a challenging and prevalent research topic, especially when the time series data are affected by complicated uncertain factors,…
The 6th AI City Challenge
Milind Naphade, Shuo Wang, David C. Anastasiu +14
The 6th edition of the AI City Challenge specifically focuses on problems in two domains where there is tremendous unlocked potential at the intersection of computer vision and art…
Synthetic Distracted Driving (SynDD2) dataset for analyzing distracted behaviors and various gaze zones of a driver
Mohammed Shaiqur Rahman, Jiyang Wang, Senem Velipasalar Gursoy +3
This article presents a synthetic distracted driving (SynDD2 - a continuum of SynDD1) dataset for machine learning models to detect and analyze drivers' various distracted behavior…
The 5th AI City Challenge
Milind Naphade, Shuo Wang, David C. Anastasiu +11
The AI City Challenge was created with two goals in mind: (1) pushing the boundaries of research and development in intelligent video analysis for smarter cities use cases, and (2)…
The 4th AI City Challenge
Milind Naphade, Shuo Wang, David Anastasiu +7
The AI City Challenge was created to accelerate intelligent video analysis that helps make cities smarter and safer. Transportation is one of the largest segments that can benefit…
CityFlow: A City-Scale Benchmark for Multi-Target Multi-Camera Vehicle Tracking and Re-Identification
Zheng Tang, Milind Naphade, Ming-Yu Liu +6
Urban traffic optimization using traffic cameras as sensors is driving the need to advance state-of-the-art multi-target multi-camera (MTMC) tracking. This work introduces CityFlow…