activity
20172025
most citedMST-GAT: A Multimodal Spatial-Temporal Graph Attention Network for Time Series Anomaly Detection

295 citations · 296 across the 7 of their papers we have counts for

collaborators

7 papers

cs.LG20251 cited

A Causality-Aware Spatiotemporal Model for Multi-Region and Multi-Pollutant Air Quality Forecasting

Junxin Lu, Shiliang Sun

Air pollution, a pressing global problem, threatens public health, environmental sustainability, and climate stability. Achieving accurate and scalable forecasting across spatially…

cs.LG2025

Revealing the Challenges of Sim-to-Real Transfer in Model-Based Reinforcement Learning via Latent Space Modeling

Zhilin Lin, Shiliang Sun

Reinforcement learning (RL) is playing an increasingly important role in fields such as robotic control and autonomous driving. However, the gap between simulation and the real env…

cs.CV2025

Multimodal Machine Translation with Visual Scene Graph Pruning

Chenyu Lu, Shiliang Sun, Jing Zhao +3

Multimodal machine translation (MMT) seeks to address the challenges posed by linguistic polysemy and ambiguity in translation tasks by incorporating visual information. A key bott…

cs.LG2025

Evaluating Menu OCR and Translation: A Benchmark for Aligning Human and Automated Evaluations in Large Vision-Language Models

Zhanglin Wu, Tengfei Song, Ning Xie +8

The rapid advancement of large vision-language models (LVLMs) has significantly propelled applications in document understanding, particularly in optical character recognition (OCR…

cs.CL2025

Memory Reviving, Continuing Learning and Beyond: Evaluation of Pre-trained Encoders and Decoders for Multimodal Machine Translation

Zhuang Yu, Shiliang Sun, Jing Zhao +2

Multimodal Machine Translation (MMT) aims to improve translation quality by leveraging auxiliary modalities such as images alongside textual input. While recent advances in large-s…

cs.LG2023295 cited

MST-GAT: A Multimodal Spatial-Temporal Graph Attention Network for Time Series Anomaly Detection

Chaoyue Ding, Shiliang Sun, Jing Zhao

Multimodal time series (MTS) anomaly detection is crucial for maintaining the safety and stability of working devices (e.g., water treatment system and spacecraft), whose data are…