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…
Fine-grained Abnormality Prompt Learning for Zero-shot Anomaly Detection
Jiawen Zhu, Yew-Soon Ong, Chunhua Shen +1
Current zero-shot anomaly detection (ZSAD) methods show remarkable success in prompting large pre-trained vision-language models to detect anomalies in a target dataset without usi…
Toward Generalist Anomaly Detection via In-context Residual Learning with Few-shot Sample Prompts
Jiawen Zhu, Guansong Pang
This paper explores the problem of Generalist Anomaly Detection (GAD), aiming to train one single detection model that can generalize to detect anomalies in diverse datasets from d…
Anomaly Heterogeneity Learning for Open-set Supervised Anomaly Detection
Jiawen Zhu, Choubo Ding, Yu Tian +1
Open-set supervised anomaly detection (OSAD) - a recently emerging anomaly detection area - aims at utilizing a few samples of anomaly classes seen during training to detect unseen…
Anomaly Detection under Distribution Shift
Tri Cao, Jiawen Zhu, Guansong Pang
Anomaly detection (AD) is a crucial machine learning task that aims to learn patterns from a set of normal training samples to identify abnormal samples in test data. Most existing…