1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.LG2025
ChronoSteer: Bridging Large Language Model and Time Series Foundation Model via Synthetic Data
Chengsen Wang, Qi Qi, Zhongwen Rao +3
Conventional forecasting methods rely on unimodal time series data, limiting their ability to exploit rich textual information. Recently, large language models (LLMs) and time seri…
cs.CL2024
ChatTime: A Unified Multimodal Time Series Foundation Model Bridging Numerical and Textual Data
Chengsen Wang, Qi Qi, Jingyu Wang +5
Human experts typically integrate numerical and textual multimodal information to analyze time series. However, most traditional deep learning predictors rely solely on unimodal nu…
cs.CV2024★ 1 cited
GroundingGPT:Language Enhanced Multi-modal Grounding Model
Zhaowei Li, Qi Xu, Dong Zhang +9
Multi-modal large language models have demonstrated impressive performance across various tasks in different modalities. However, existing multi-modal models primarily emphasize ca…