5 citations · 21 across the 25 of their papers we have counts for
27 papers
Beyond English: Uncovering the Multilingual Gap in Vision-Language-Action Models
Hanyang Chen, Hongliang Li, Jiarui Cao +6
Vision-Language-Action models have recently demonstrated promising capabilities in learning generalist robot policies from large-scale multimodal data. However, most existing VLA s…
SAMoRA: Semantic-Aware Mixture of LoRA Experts for Task-Adaptive Learning
Boyan Shi, Wei Chen, Shuyuan Zhao +4
The combination of Mixture-of-Experts (MoE) and Low-Rank Adaptation (LoRA) has shown significant potential for enhancing the multi-task learning capabilities of Large Language Mode…
STK-Adapter: Incorporating Evolving Graph and Event Chain for Temporal Knowledge Graph Extrapolation
Shuyuan Zhao, Wei Chen, Weijie Zhang +6
Temporal Knowledge Graph (TKG) extrapolation aims to predict future events based on historical facts. Recent studies have attempted to enhance TKG extrapolation by integrating TKG'…
Zoom In, Reason Out: Efficient Far-field Anomaly Detection in Expressway Surveillance Videos via Focused VLM Reasoning Guided by Bayesian Inference
Xiaowei Mao, Bowen Sui, Weijie Zhang +7
Expressway video anomaly detection is important for traffic safety, but remains challenging across diverse scenes, particularly for far-field vehicles with subtle abnormal motion.…
DiSGMM: A Method for Time-varying Microscopic Weight Completion on Road Networks
Yan Lin, Jilin Hu, Shengnan Guo +3
Microscopic road-network weights represent fine-grained, time-varying traffic conditions obtained from individual vehicles. An example is travel speeds associated with road segment…
Spatial-Temporal Feedback Diffusion Guidance for Controlled Traffic Imputation
Xiaowei Mao, Huihu Ding, Yan Lin +6
Imputing missing values in spatial-temporal traffic data is essential for intelligent transportation systems. Among advanced imputation methods, score-based diffusion models have d…
RIPCN: A Road Impedance Principal Component Network for Probabilistic Traffic Flow Forecasting
Haochen Lv, Yan Lin, Shengnan Guo +5
Accurate traffic flow forecasting is crucial for intelligent transportation services such as navigation and ride-hailing. In such applications, uncertainty estimation in forecastin…
TrajMamba: An Efficient and Semantic-rich Vehicle Trajectory Pre-training Model
Yichen Liu, Yan Lin, Shengnan Guo +3
Vehicle GPS trajectories record how vehicles move over time, storing valuable travel semantics, including movement patterns and travel purposes. Learning travel semantics effective…
TransferTraj: A Vehicle Trajectory Learning Model for Region and Task Transferability
Tonglong Wei, Yan Lin, Zeyu Zhou +6
Vehicle GPS trajectories provide valuable movement information that supports various downstream tasks and applications. A desirable trajectory learning model should be able to tran…
Towards An Efficient and Effective En Route Travel Time Estimation Framework
Zekai Shen, Haitao Yuan, Xiaowei Mao +4
En route travel time estimation (ER-TTE) focuses on predicting the travel time of the remaining route. Existing ER-TTE methods always make re-estimation which significantly hinders…
A Survey and Benchmarking of Spatial-Temporal Traffic Data Imputation Models
Shengnan Guo, Tonglong Wei, Yiheng Huang +6
Traffic data imputation is a critical preprocessing step in intelligent transportation systems, underpinning the reliability of downstream transportation services. Despite substant…
DiffLight: A Partial Rewards Conditioned Diffusion Model for Traffic Signal Control with Missing Data
Hanyang Chen, Yang Jiang, Shengnan Guo +3
The application of reinforcement learning in traffic signal control (TSC) has been extensively researched and yielded notable achievements. However, most existing works for TSC ass…
Mobility-LLM: Learning Visiting Intentions and Travel Preferences from Human Mobility Data with Large Language Models
Letian Gong, Yan Lin, Xinyue Zhang +6
Location-based services (LBS) have accumulated extensive human mobility data on diverse behaviors through check-in sequences. These sequences offer valuable insights into users' in…
PLMTrajRec: A Scalable and Generalizable Trajectory Recovery Method with Pre-trained Language Models
Tonglong Wei, Yan Lin, Youfang Lin +5
Spatiotemporal trajectory data is crucial for various applications. However, issues such as device malfunctions and network instability often cause sparse trajectories, leading to…
TrajFM: A Vehicle Trajectory Foundation Model for Region and Task Transferability
Yan Lin, Tonglong Wei, Zeyu Zhou +5
Vehicle trajectories provide valuable movement information that supports various downstream tasks and powers real-world applications. A desirable trajectory learning model should t…
PTrajM: Efficient and Semantic-rich Trajectory Learning with Pretrained Trajectory-Mamba
Yan Lin, Yichen Liu, Zeyu Zhou +5
Vehicle trajectories provide crucial movement information for various real-world applications. To better utilize vehicle trajectories, it is essential to develop a trajectory learn…
DutyTTE: Deciphering Uncertainty in Origin-Destination Travel Time Estimation
Xiaowei Mao, Yan Lin, Shengnan Guo +6
Uncertainty quantification in travel time estimation (TTE) aims to estimate the confidence interval for travel time, given the origin (O), destination (D), and departure time (T).…
Spatial-Temporal Cross-View Contrastive Pre-training for Check-in Sequence Representation Learning
Letian Gong, Huaiyu Wan, Shengnan Guo +6
The rapid growth of location-based services (LBS) has yielded massive amounts of data on human mobility. Effectively extracting meaningful representations for user-generated check-…
UniTE: A Survey and Unified Pipeline for Pre-training Spatiotemporal Trajectory Embeddings
Yan Lin, Zeyu Zhou, Yicheng Liu +8
Spatiotemporal trajectories are sequences of timestamped locations, which enable a variety of analyses that in turn enable important real-world applications. It is common to map tr…
STD-PLM: Understanding Both Spatial and Temporal Properties of Spatial-Temporal Data with PLM
YiHeng Huang, Xiaowei Mao, Shengnan Guo +5
Spatial-temporal forecasting and imputation are important for real-world intelligent systems. Most existing methods are tailored for individual forecasting or imputation tasks but…
TrajCogn: Leveraging LLMs for Cognizing Movement Patterns and Travel Purposes from Trajectories
Zeyu Zhou, Yan Lin, Haomin Wen +5
Spatio-temporal trajectories are crucial in various data mining tasks. It is important to develop a versatile trajectory learning method that performs different tasks with high acc…
Micro-Macro Spatial-Temporal Graph-based Encoder-Decoder for Map-Constrained Trajectory Recovery
Tonglong Wei, Youfang Lin, Yan Lin +3
Recovering intermediate missing GPS points in a sparse trajectory, while adhering to the constraints of the road network, could offer deep insights into users' moving behaviors in…
Diff-RNTraj: A Structure-aware Diffusion Model for Road Network-constrained Trajectory Generation
Tonglong Wei, Youfang Lin, Shengnan Guo +5
Trajectory data is essential for various applications as it records the movement of vehicles. However, publicly available trajectory datasets remain limited in scale due to privacy…
UVTM: Universal Vehicle Trajectory Modeling with ST Feature Domain Generation
Yan Lin, Jilin Hu, Shengnan Guo +4
Vehicle movement is frequently captured in the form of GPS trajectories, i.e., sequences of timestamped GPS locations. Such data is widely used for various tasks such as travel-tim…