activity
20232025
most citedSpatial-Temporal Large Language Model for Traffic Prediction

10 citations · 40 across the 26 of their papers we have counts for

collaborators
Showing 2024Show all

12 papers · 1 filter

cs.MA2024

DTPPO: Dual-Transformer Encoder-based Proximal Policy Optimization for Multi-UAV Navigation in Unseen Complex Environments

Anning Wei, Jintao Liang, Kaiyuan Lin +2

Existing multi-agent deep reinforcement learning (MADRL) methods for multi-UAV navigation face challenges in generalization, particularly when applied to unseen complex environment…

cs.CV2024★ 2 cited

Hybrid Mamba for Few-Shot Segmentation

Qianxiong Xu, Xuanyi Liu, Lanyun Zhu +4

Many few-shot segmentation (FSS) methods use cross attention to fuse support foreground (FG) into query features, regardless of the quadratic complexity. A recent advance Mamba can…

cs.MA2024

GuideLight: "Industrial Solution" Guidance for More Practical Traffic Signal Control Agents

Haoyuan Jiang, Xuantang Xiong, Ziyue Li +7

Currently, traffic signal control (TSC) methods based on reinforcement learning (RL) have proven superior to traditional methods. However, most RL methods face difficulties when ap…

cs.CV2024★ 1 cited

Eliminating Feature Ambiguity for Few-Shot Segmentation

Qianxiong Xu, Guosheng Lin, Chen Change Loy +3

Recent advancements in few-shot segmentation (FSS) have exploited pixel-by-pixel matching between query and support features, typically based on cross attention, which selectively…

cs.MA2024★ 2 cited

CoSLight: Co-optimizing Collaborator Selection and Decision-making to Enhance Traffic Signal Control

Jingqing Ruan, Ziyue Li, Hua Wei +5

Effective multi-intersection collaboration is pivotal for reinforcement-learning-based traffic signal control to alleviate congestion. Existing work mainly chooses neighboring inte…

cs.LG2024★ 3 cited

TimeCMA: Towards LLM-Empowered Multivariate Time Series Forecasting via Cross-Modality Alignment

Chenxi Liu, Qianxiong Xu, Hao Miao +5

Multivariate time series forecasting (MTSF) aims to learn temporal dynamics among variables to forecast future time series. Existing statistical and deep learning-based methods suf…

cs.DB2024

SQL-to-Schema Enhances Schema Linking in Text-to-SQL

Sun Yang, Qiong Su, Zhishuai Li +4

In sophisticated existing Text-to-SQL methods exhibit errors in various proportions, including schema-linking errors (incorrect columns, tables, or extra columns), join errors, nes…

cs.AI2024★ 1 cited

X-Light: Cross-City Traffic Signal Control Using Transformer on Transformer as Meta Multi-Agent Reinforcement Learner

Haoyuan Jiang, Ziyue Li, Hua Wei +5

The effectiveness of traffic light control has been significantly improved by current reinforcement learning-based approaches via better cooperation among multiple traffic lights.…

cs.CL2024★ 3 cited

PET-SQL: A Prompt-Enhanced Two-Round Refinement of Text-to-SQL with Cross-consistency

Zhishuai Li, Xiang Wang, Jingjing Zhao +8

Recent advancements in Text-to-SQL (Text2SQL) emphasize stimulating the large language models (LLM) on in-context learning, achieving significant results. Nevertheless, they face c…

cs.CL2024★ 9 cited

SQLBench: A Comprehensive Evaluation for Text-to-SQL Capabilities of Large Language Models

Bin Zhang, Yuxiao Ye, Guoqing Du +8

Large Language Models (LLMs) have emerged as a powerful tool in advancing the Text-to-SQL task, significantly outperforming traditional methods.Nevertheless, as a nascent research…

cs.LG2024

Non-Neighbors Also Matter to Kriging: A New Contrastive-Prototypical Learning

Zhishuai Li, Yunhao Nie, Ziyue Li +3

Kriging aims at estimating the attributes of unsampled geo-locations from observations in the spatial vicinity or physical connections, which helps mitigate skewed monitoring cause…

cs.LG2024★ 10 cited

Spatial-Temporal Large Language Model for Traffic Prediction

Chenxi Liu, Sun Yang, Qianxiong Xu +4

Traffic prediction, an essential component for intelligent transportation systems, endeavours to use historical data to foresee future traffic features at specific locations. Altho…