18 citations · 38 across the 10 of their papers we have counts for
10 papers
DUSA: Decoupled Unsupervised Sim2Real Adaptation for Vehicle-to-Everything Collaborative Perception
Xianghao Kong, Wentao Jiang, Jinrang Jia +3
Vehicle-to-Everything (V2X) collaborative perception is crucial for autonomous driving. However, achieving high-precision V2X perception requires a significant amount of annotated…
V2X-Seq: A Large-Scale Sequential Dataset for Vehicle-Infrastructure Cooperative Perception and Forecasting
Haibao Yu, Wenxian Yang, Hongzhi Ruan +11
Utilizing infrastructure and vehicle-side information to track and forecast the behaviors of surrounding traffic participants can significantly improve decision-making and safety i…
Open-TransMind: A New Baseline and Benchmark for 1st Foundation Model Challenge of Intelligent Transportation
Yifeng Shi, Feng Lv, Xinliang Wang +5
With the continuous improvement of computing power and deep learning algorithms in recent years, the foundation model has grown in popularity. Because of its powerful capabilities…
Evaluation of Pedestrian Safety in a High-Fidelity Simulation Environment Framework
Lin Ma, Longrui Chen, Yan Zhang +9
Pedestrians' safety is a crucial factor in assessing autonomous driving scenarios. However, pedestrian safety evaluation is rarely considered by existing autonomous driving simulat…
DAIR-V2X: A Large-Scale Dataset for Vehicle-Infrastructure Cooperative 3D Object Detection
Haibao Yu, Yizhen Luo, Mao Shu +8
Autonomous driving faces great safety challenges for a lack of global perspective and the limitation of long-range perception capabilities. It has been widely agreed that vehicle-i…
Rope3D: TheRoadside Perception Dataset for Autonomous Driving and Monocular 3D Object Detection Task
Xiaoqing Ye, Mao Shu, Hanyu Li +5
Concurrent perception datasets for autonomous driving are mainly limited to frontal view with sensors mounted on the vehicle. None of them is designed for the overlooked roadside p…
DSANet: Dynamic Segment Aggregation Network for Video-Level Representation Learning
Wenhao Wu, Yuxiang Zhao, Yanwu Xu +8
Long-range and short-range temporal modeling are two complementary and crucial aspects of video recognition. Most of the state-of-the-arts focus on short-range spatio-temporal mode…
Deep Goal-Oriented Clustering
Yifeng Shi, Christopher M. Bender, Junier B. Oliva +1
Clustering and prediction are two primary tasks in the fields of unsupervised and supervised learning, respectively. Although much of the recent advances in machine learning have b…
Defense Through Diverse Directions
Christopher M. Bender, Yang Li, Yifeng Shi +2
In this work we develop a novel Bayesian neural network methodology to achieve strong adversarial robustness without the need for online adversarial training. Unlike previous effor…
Deep Message Passing on Sets
Yifeng Shi, Junier Oliva, Marc Niethammer
Modern methods for learning over graph input data have shown the fruitfulness of accounting for relationships among elements in a collection. However, most methods that learn over…