397 citations · 695 across the 41 of their papers we have counts for
41 papers
Learning Optimal Robust Control of Connected Vehicles in Mixed Traffic Flow
Jie Li, Jiawei Wang, Shengbo Eben Li +1
Connected and automated vehicles (CAVs) technologies promise to attenuate undesired traffic disturbances. However, in mixed traffic where human-driven vehicles (HDVs) also exist, t…
Lifelike Agility and Play in Quadrupedal Robots using Reinforcement Learning and Generative Pre-trained Models
Lei Han, Qingxu Zhu, Jiapeng Sheng +16
Knowledge from animals and humans inspires robotic innovations. Numerous efforts have been made to achieve agile locomotion in quadrupedal robots through classical controllers or r…
MMNet: Multi-Collaboration and Multi-Supervision Network for Sequential Deepfake Detection
Ruiyang Xia, Decheng Liu, Jie Li +3
Advanced manipulation techniques have provided criminals with opportunities to make social panic or gain illicit profits through the generation of deceptive media, such as forged f…
SMC-UDA: Structure-Modal Constraint for Unsupervised Cross-Domain Renal Segmentation
Zhusi Zhong, Jie Li, Lulu Bi +6
Medical image segmentation based on deep learning often fails when deployed on images from a different domain. The domain adaptation methods aim to solve domain-shift challenges, b…
Language Knowledge-Assisted Representation Learning for Skeleton-Based Action Recognition
Haojun Xu, Yan Gao, Zheng Hui +2
How humans understand and recognize the actions of others is a complex neuroscientific problem that involves a combination of cognitive mechanisms and neural networks. Research has…
Tracking through Containers and Occluders in the Wild
Basile Van Hoorick, Pavel Tokmakov, Simon Stent +2
Tracking objects with persistence in cluttered and dynamic environments remains a difficult challenge for computer vision systems. In this paper, we introduce , a ne…
Weakly-Supervised Temporal Action Localization with Bidirectional Semantic Consistency Constraint
Guozhang Li, De Cheng, Xinpeng Ding +3
Weakly Supervised Temporal Action Localization (WTAL) aims to classify and localize temporal boundaries of actions for the video, given only video-level category labels in the trai…
Standing Between Past and Future: Spatio-Temporal Modeling for Multi-Camera 3D Multi-Object Tracking
Ziqi Pang, Jie Li, Pavel Tokmakov +3
This work proposes an end-to-end multi-camera 3D multi-object tracking (MOT) framework. It emphasizes spatio-temporal continuity and integrates both past and future reasoning for t…
What's in a Name? Beyond Class Indices for Image Recognition
Kai Han, Xiaohu Huang, Yandong Li +3
Existing machine learning models demonstrate excellent performance in image object recognition after training on a large-scale dataset under full supervision. However, these models…
Traffic4cast at NeurIPS 2022 -- Predict Dynamics along Graph Edges from Sparse Node Data: Whole City Traffic and ETA from Stationary Vehicle Detectors
Moritz Neun, Christian Eichenberger, Henry Martin +27
The global trends of urbanization and increased personal mobility force us to rethink the way we live and use urban space. The Traffic4cast competition series tackles this problem…
Adaptive incentive for cross-silo federated learning: A multi-agent reinforcement learning approach
Shijing Yuan, Hongze Liu, Hongtao Lv +4
Cross-silo federated learning (FL) is a typical FL that enables organizations(e.g., financial or medical entities) to train global models on isolated data. Reasonable incentive is…
Maximal co-occurrence nonoverlapping sequential rule mining
Yan Li, Chang Zhang, Jie Li +4
The aim of sequential pattern mining (SPM) is to discover potentially useful information from a given se-quence. Although various SPM methods have been investigated, most of these…
Multi-task Learning for Sparse Traffic Forecasting
Jiezhang Li, Junjun Li, Yue-Jiao Gong
Accurate traffic prediction is crucial to improve the performance of intelligent transportation systems. Previous traffic prediction tasks mainly focus on small and non-isolated tr…
ShaSTA: Modeling Shape and Spatio-Temporal Affinities for 3D Multi-Object Tracking
Tara Sadjadpour, Jie Li, Rares Ambrus +1
Multi-object tracking is a cornerstone capability of any robotic system. The quality of tracking is largely dependent on the quality of the detector used. In many applications, suc…
Automatic Change-Point Detection in Time Series via Deep Learning
Jie Li, Paul Fearnhead, Piotr Fryzlewicz +1
Detecting change-points in data is challenging because of the range of possible types of change and types of behaviour of data when there is no change. Statistically efficient meth…
Depth Is All You Need for Monocular 3D Detection
Dennis Park, Jie Li, Dian Chen +2
A key contributor to recent progress in 3D detection from single images is monocular depth estimation. Existing methods focus on how to leverage depth explicitly, by generating pse…
Seen to Unseen: When Fuzzy Inference System Predicts IoT Device Positioning Labels That Had Not Appeared in Training Phase
Han Xu, Zheming Zuo, Jie Li +1
Situating at the core of Artificial Intelligence (AI), Machine Learning (ML), and more specifically, Deep Learning (DL) have embraced great success in the past two decades. However…
Seeking Subjectivity in Visual Emotion Distribution Learning
Jingyuan Yang, Jie Li, Leida Li +3
Visual Emotion Analysis (VEA), which aims to predict people's emotions towards different visual stimuli, has become an attractive research topic recently. Rather than a single labe…
SpOT: Spatiotemporal Modeling for 3D Object Tracking
Colton Stearns, Davis Rempe, Jie Li +5
3D multi-object tracking aims to uniquely and consistently identify all mobile entities through time. Despite the rich spatiotemporal information available in this setting, current…
TransFA: Transformer-based Representation for Face Attribute Evaluation
Decheng Liu, Weijie He, Chunlei Peng +3
Face attribute evaluation plays an important role in video surveillance and face analysis. Although methods based on convolution neural networks have made great progress, they inev…
On Collective Robustness of Bagging Against Data Poisoning
Ruoxin Chen, Zenan Li, Jie Li +2
Bootstrap aggregating (bagging) is an effective ensemble protocol, which is believed can enhance robustness by its majority voting mechanism. Recent works further prove the sample-…
On Understanding and Mitigating the Dimensional Collapse of Graph Contrastive Learning: a Non-Maximum Removal Approach
Jiawei Sun, Ruoxin Chen, Jie Li +3
Graph Contrastive Learning (GCL) has shown promising performance in graph representation learning (GRL) without the supervision of manual annotations. GCL can generate graph-level…
DPointNet: A Density-Oriented PointNet for 3D Object Detection in Point Clouds
Jie Li, Yu Hu
For current object detectors, the scale of the receptive field of feature extraction operators usually increases layer by layer. Those operators are called scale-oriented operators…
Curvature-based Feature Selection with Application in Classifying Electronic Health Records
Zheming Zuo, Jie Li, Han Xu +1
Disruptive technologies provides unparalleled opportunities to contribute to the identifications of many aspects in pervasive healthcare, from the adoption of the Internet of Thing…