Do LLMs Understand Visual Anomalies? Uncovering LLM's Capabilities in Zero-shot Anomaly Detection
Large vision-language models (LVLMs) are markedly proficient in deriving visual
representations guided by natural language. Recent explorations have utilized LVLMs to …
representations guided by natural language. Recent explorations have utilized LVLMs to …
Long-term urban air quality prediction with hierarchical attention loop network
Air pollution poses severe threats to human health, socioeconomic development, and
natural environment, making it one of the most serious environmental issues. Accurate long …
natural environment, making it one of the most serious environmental issues. Accurate long …
Revisiting streaming anomaly detection: benchmark and evaluation
Anomaly detection in streaming data is an important task for many real-world applications,
such as network security, fraud detection, and system monitoring. However, streaming data …
such as network security, fraud detection, and system monitoring. However, streaming data …
OEBench: Investigating Open Environment Challenges in Real-World Relational Data Streams
Relational datasets are widespread in real-world scenarios and are usually delivered in a
streaming fashion. This type of data stream can present unique challenges, such as …
streaming fashion. This type of data stream can present unique challenges, such as …
Towards a Zero-Day Anomaly Detector in Cyber Physical Systems Using a Hybrid VAE-LSTM-OCSVM Model
R Yatagha, B Nebebe, K Waedt, C Ruland - Proceedings of the 33rd …, 2024 - dl.acm.org
Despite the growing volume of time series data across various domains, detecting
anomalies remains challenging due to the complexity and dynamic nature of the data …
anomalies remains challenging due to the complexity and dynamic nature of the data …
Proactive Model Adaptation Against Concept Drift for Online Time Series Forecasting
Time series forecasting always faces the challenge of concept drift, where data distributions
evolve over time, leading to a decline in forecast model performance. Existing solutions are …
evolve over time, leading to a decline in forecast model performance. Existing solutions are …
Online Anomaly Detection for Streaming Data in the Presence of Missing Values
X Xu, M Liu, X Cheng, L Song… - 2024 IEEE International …, 2024 - ieeexplore.ieee.org
Online anomaly detection is a critical area in data analysis, particularly for handling dynamic
data streams and addressing the challenge of concept drift. While current methods for online …
data streams and addressing the challenge of concept drift. While current methods for online …
[CITATION][C] M-TTA: Masked Autoencoder 기반 Test-Time Adaptation 을 활용한 다변량 시계열 이상탐지
하윤지， 백준걸 - 대한산업공학회 추계학술대회 논문집, 2024 - dbpia.co.kr
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