2 citations · 6 across the 9 of their papers we have counts for
10 papers
POEM: Phase-Aware Feature Rotation for Time Series Forecasting Under Periodicity Drift
Jiawen Zhu, Shuhan Liu, Shengxuan Li +2
Deep learning has advanced time series forecasting, but periodicity drift, in which cycle timing and phase vary over time, remains a challenging problem. Existing methods predomina…
AnomalyAgent: Training-Free Agentic Models for Zero-/Few-Shot Anomaly Detection
Yi Zhang, Jiawen Zhu, Lele Fu +1
Benefiting from generalizability of vision-language models (VLMs) such as CLIP, many zero-/few-shot anomaly detection (AD) approaches have achieved impressive detection performance…
Dynamic TMoE: A Drift-Aware Dynamic Mixture of Experts Framework for Non-Stationary Time Series Forecasting
Jiawen Zhu, Shuhan Liu, Di Weng +1
Non-stationary time series forecasting is challenged by evolving distribution shifts that static models struggle to capture. While Mixture-of-Experts (MoE) architectures offer a pr…
InCTRLv2: Generalist Residual Models for Few-Shot Anomaly Detection and Segmentation
Jiawen Zhu, Mengjia Niu, Guansong Pang
While recent anomaly detection (AD) methods have made substantial progress in recognizing abnormal patterns within specific domains, most of them are specialist models that are tra…
Unleashing Vision-Language Semantics for Deepfake Video Detection
Jiawen Zhu, Yunqi Miao, Xueyi Zhang +2
Recent Deepfake Video Detection (DFD) studies have demonstrated that pre-trained Vision-Language Models (VLMs) such as CLIP exhibit strong generalization capabilities in detecting…
Adapting Large Language Models for Parameter-Efficient Log Anomaly Detection
Ying Fu Lim, Jiawen Zhu, Guansong Pang
Log Anomaly Detection (LAD) seeks to identify atypical patterns in log data that are crucial to assessing the security and condition of systems. Although Large Language Models (LLM…