211 citations · 339 across the 14 of their papers we have counts for
14 papers
Car-GS: Addressing Reflective and Transparent Surface Challenges in 3D Car Reconstruction
Congcong Li, Jin Wang, Xiaomeng Wang +4
3D car modeling is crucial for applications in autonomous driving systems, virtual and augmented reality, and gaming. However, due to the distinctive properties of cars, such as hi…
STT: Stateful Tracking with Transformers for Autonomous Driving
Longlong Jing, Ruichi Yu, Xu Chen +19
Tracking objects in three-dimensional space is critical for autonomous driving. To ensure safety while driving, the tracker must be able to reliably track objects across frames and…
Pedestrian Crossing Action Recognition and Trajectory Prediction with 3D Human Keypoints
Jiachen Li, Xinwei Shi, Feiyu Chen +9
Accurate understanding and prediction of human behaviors are critical prerequisites for autonomous vehicles, especially in highly dynamic and interactive scenarios such as intersec…
SC-Transformer++: Structured Context Transformer for Generic Event Boundary Detection
Dexiang Hong, Xiaoqi Ma, Xinyao Wang +3
This report presents the algorithm used in the submission of Generic Event Boundary Detection (GEBD) Challenge at CVPR 2022. In this work, we improve the existing Structured Contex…
End-to-End Compressed Video Representation Learning for Generic Event Boundary Detection
Congcong Li, Xinyao Wang, Longyin Wen +3
Generic event boundary detection aims to localize the generic, taxonomy-free event boundaries that segment videos into chunks. Existing methods typically require video frames to be…
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,…
Generic Event Boundary Detection Challenge at CVPR 2021 Technical Report: Cascaded Temporal Attention Network (CASTANET)
Dexiang Hong, Congcong Li, Longyin Wen +2
This report presents the approach used in the submission of Generic Event Boundary Detection (GEBD) Challenge at CVPR21. In this work, we design a Cascaded Temporal Attention Netwo…
TNT: Target-driveN Trajectory Prediction
Hang Zhao, Jiyang Gao, Tian Lan +9
Predicting the future behavior of moving agents is essential for real world applications. It is challenging as the intent of the agent and the corresponding behavior is unknown and…
VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation
Jiyang Gao, Chen Sun, Hang Zhao +4
Behavior prediction in dynamic, multi-agent systems is an important problem in the context of self-driving cars, due to the complex representations and interactions of road compone…
STINet: Spatio-Temporal-Interactive Network for Pedestrian Detection and Trajectory Prediction
Zhishuai Zhang, Jiyang Gao, Junhua Mao +3
Detecting pedestrians and predicting future trajectories for them are critical tasks for numerous applications, such as autonomous driving. Previous methods either treat the detect…
Improving 3D Object Detection through Progressive Population Based Augmentation
Shuyang Cheng, Zhaoqi Leng, Ekin Dogus Cubuk +10
Data augmentation has been widely adopted for object detection in 3D point clouds. However, all previous related efforts have focused on manually designing specific data augmentati…
Spatial Attention Pyramid Network for Unsupervised Domain Adaptation
Congcong Li, Dawei Du, Libo Zhang +4
Unsupervised domain adaptation is critical in various computer vision tasks, such as object detection, instance segmentation, and semantic segmentation, which aims to alleviate per…
Data Priming Network for Automatic Check-Out
Congcong Li, Dawei Du, Libo Zhang +5
Automatic Check-Out (ACO) receives increased interests in recent years. An important component of the ACO system is the visual item counting, which recognizes the categories and co…
Towards Holistic Scene Understanding: Feedback Enabled Cascaded Classification Models
Congcong Li, Adarsh Kowdle, Ashutosh Saxena +1
Scene understanding includes many related sub-tasks, such as scene categorization, depth estimation, object detection, etc. Each of these sub-tasks is often notoriously hard, and s…