10 citations · 47 across the 33 of their papers we have counts for
34 papers
From Causal Discovery to Implementation: An Agentic AI Framework for E-Scooter Mobility Hub Planning Across 29 German Cities
Meng Jin, Melanie Handrich, Simone Martinenz +2
Existing approaches to e-scooter mobility hub planning lack city-type-specific causal evidence. Demand models are typically correlational, built on proprietary trip data, and do no…
One Image is All You Need: Agentic One-Shot Image Generation via Text-Based World Models for Long-Tail Spatial Perception
Keqin Zeng, Shuting Su, Shihao Lin +2
Reliable spatial decision automation, such as autonomous driving and maritime surveillance, critically depends on robust visual perception. However, real-world spatiotemporal data…
Multi-Modal Contrastive Learning for Implicit Earth Embeddings via Location Tying
Jonathan Hecht, Lukas Arzoumanidis, Ziyue Li +1
Spatial prediction tasks are often limited by a lack of high-quality labelled ground-truth observations. To overcome this challenge, self-supervised pre-training is a possible solu…
GTR-Bench: Evaluating Geo-Temporal Reasoning in Vision-Language Models
Qinghongbing Xie, Zhaoyuan Xia, Feng Zhu +4
Recently spatial-temporal intelligence of Visual-Language Models (VLMs) has attracted much attention due to its importance for autonomous driving, embodied AI and general AI. Exist…
Fishing for Answers: Exploring One-shot vs. Iterative Retrieval Strategies for Retrieval Augmented Generation
Huifeng Lin, Gang Su, Jintao Liang +3
Retrieval-Augmented Generation (RAG) based on Large Language Models (LLMs) is a powerful solution to understand and query the industry's closed-source documents. However, basic RAG…
Language-Instructed Reasoning for Group Activity Detection via Multimodal Large Language Model
Jihua Peng, Qianxiong Xu, Yichen Liu +4
Group activity detection (GAD) aims to simultaneously identify group members and categorize their collective activities within video sequences. Existing deep learning-based methods…
SAMITE: Position Prompted SAM2 with Calibrated Memory for Visual Object Tracking
Qianxiong Xu, Lanyun Zhu, Chenxi Liu +4
Visual Object Tracking (VOT) is widely used in applications like autonomous driving to continuously track targets in videos. Existing methods can be roughly categorized into templa…
LLMs Meet Cross-Modal Time Series Analytics: Overview and Directions
Chenxi Liu, Hao Miao, Cheng Long +3
Large Language Models (LLMs) have emerged as a promising paradigm for time series analytics, leveraging their massive parameters and the shared sequential nature of textual and tim…
Reasoning RAG via System 1 or System 2: A Survey on Reasoning Agentic Retrieval-Augmented Generation for Industry Challenges
Jintao Liang, Gang Su, Huifeng Lin +3
Retrieval-Augmented Generation (RAG) has emerged as a powerful framework to overcome the knowledge limitations of Large Language Models (LLMs) by integrating external retrieval wit…
Unlocking the Power of SAM 2 for Few-Shot Segmentation
Qianxiong Xu, Lanyun Zhu, Xuanyi Liu +4
Few-Shot Segmentation (FSS) aims to learn class-agnostic segmentation on few classes to segment arbitrary classes, but at the risk of overfitting. To address this, some methods use…
Efficient Multivariate Time Series Forecasting via Calibrated Language Models with Privileged Knowledge Distillation
Chenxi Liu, Hao Miao, Qianxiong Xu +5
Multivariate time series forecasting (MTSF) endeavors to predict future observations given historical data, playing a crucial role in time series data management systems. With adva…
Towards Cross-Modality Modeling for Time Series Analytics: A Survey in the LLM Era
Chenxi Liu, Shaowen Zhou, Qianxiong Xu +4
The proliferation of edge devices has generated an unprecedented volume of time series data across different domains, motivating various well-customized methods. Recently, Large La…
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…
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…
Transformer-based Drum-level Prediction in a Boiler Plant with Delayed Relations among Multivariates
Gang Su, Sun Yang, Zhishuai Li
The steam drum water level is a critical parameter that directly impacts the safety and efficiency of power plant operations. However, predicting the drum water level in boilers is…
Deep Causal Learning to Explain and Quantify The Geo-Tension's Impact on Natural Gas Market
Philipp Kai Peter, Yulin Li, Ziyue Li +1
Natural gas demand is a crucial factor for predicting natural gas prices and thus has a direct influence on the power system. However, existing methods face challenges in assessing…
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…
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…
TraffiDent: A Dataset for Understanding the Interplay Between Traffic Dynamics and Incidents
Xiaochuan Gou, Ziyue Li, Tian Lan +6
Long-separated research has been conducted on two highly correlated tracks: traffic and incidents. Traffic track witnesses complicating deep learning models, e.g., to push the pred…
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…
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…
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…
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.…
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…