collaborators

6 papers

cs.CV2025

Temporal-Visual Semantic Alignment: A Unified Architecture for Transferring Spatial Priors from Vision Models to Zero-Shot Temporal Tasks

Xiangkai Ma, Han Zhang, Wenzhong Li +1

Large Multimodal Models (LMMs) have achieved remarkable progress in aligning and generating content across text and image modalities. However, the potential of using non-visual, co…

cs.RO2025

Unifying Perception and Action: A Hybrid-Modality Pipeline with Implicit Visual Chain-of-Thought for Robotic Action Generation

Xiangkai Ma, Lekai Xing, Han Zhang +2

Vision-Language-Action (VLA) models built upon Chain-of-Thought (CoT) have achieved remarkable success in advancing general-purpose robotic agents, owing to its significant percept…

cs.AI2025

Energy-Aware Pattern Disentanglement: A Generalizable Pattern Assisted Architecture for Multi-task Time Series Analysis

Xiangkai Ma, Xiaobin Hong, Wenzhong Li +1

Time series analysis has found widespread applications in areas such as weather forecasting, anomaly detection, and healthcare. While deep learning approaches have achieved signifi…

cs.LG2025

Unify and Anchor: A Context-Aware Transformer for Cross-Domain Time Series Forecasting

Xiaobin Hong, Jiawen Zhang, Wenzhong Li +2

The rise of foundation models has revolutionized natural language processing and computer vision, yet their best practices to time series forecasting remains underexplored. Existin…

cs.LG2024

Domain Fusion Controllable Generalization for Cross-Domain Time Series Forecasting from Multi-Domain Integrated Distribution

Xiangkai Ma, Xiaobin Hong, Mingkai Lin +3

Conventional deep models have achieved unprecedented success in time series forecasting. However, facing the challenge of cross-domain generalization, existing studies utilize stat…

cs.LG2024

A Wave is Worth 100 Words: Investigating Cross-Domain Transferability in Time Series

Xiangkai Ma, Xiaobin Hong, Wenzhong Li +1

Time series analysis is a fundamental data mining task that supervised training methods based on empirical risk minimization have proven their effectiveness on specific tasks and d…