Portrait de Ali Karami

Ali Karami

Alumni
Google Scholar

Publications

Graph-Jigsaw Conditioned Diffusion Model for Skeleton-based Video Anomaly Detection
Ali Karami
Thi Kieu Khanh Ho
Narges Armanfard
2025-03-06
2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) (publié)
doi.org
arxiv.org
Graph Anomaly Detection in Time Series: A Survey.
Thi Kieu Khanh Ho
Ali Karami
Narges Armanfard
2025-01-01
IEEE Transactions on Pattern Analysis and Machine Intelligence (publié)
doi.org
arxiv.org
Graph-Jigsaw Conditioned Diffusion Model for Skeleton-Based Video Anomaly Detection
Ali Karami
Thi Kieu Khanh Ho
Narges Armanfard
Skeleton-based video anomaly detection (SVAD) is a crucial task in computer vision. Accurately identifying abnormal patterns or events enabl… (voir plus)es operators to promptly de-tect suspicious activities, thereby enhancing safety. Achieving this demands a comprehensive understanding of human motions, both at body and region levels, while also accounting for the wide variations of performing a single action. However, existing studies fail to simultaneously address these crucial properties. This paper introduces a novel, practical, and lightweight framework, namely Graph-Jigsaw Conditioned Diffusion Model for Skeleton-based Video Anomaly Detection (GiCiSAD) to overcome the challenges associated with SVAD. GiCiSAD consists of three novel modules: the Graph Attention-based Forecasting module to capture the spatio-temporal dependencies inherent in the data, the Graph-level Jigsaw Puzzle Maker module to distinguish subtle region-level discrepancies between normal and abnormal motions, and the Graph-based Conditional Diffusion model to generate a wide spectrum of human motions. Extensive experiments on four widely used skeleton-based video datasets show that GiCiSAD outperforms existing methods with significantly fewer training parameters, establishing it as the new state-of-the-art.
2024-03-18
ArXiv (prépublication)
doi.org
arxiv.org
Graph-based Time-Series Anomaly Detection: A Survey
Thi Kieu Khanh Ho
Ali Karami
Narges Armanfard
With the recent advances in technology, a wide range of systems continue to collect a large amount of data over time and thus generate time … (voir plus)series. Time-Series Anomaly Detection (TSAD) is an important task in various time-series applications such as e-commerce, cybersecurity, vehicle maintenance, and healthcare monitoring. However, this task is very challenging as it requires considering both the intra-variable dependency and the inter-variable dependency, where a variable can be defined as an observation in time series data. Recent graph-based approaches have made impressive progress in tackling the challenges of this field. In this survey, we conduct a comprehensive and up-to-date review of Graph-based TSAD (G-TSAD). First, we explore the significant potential of graph representation learning for time-series data. Then, we review state-of-the-art graph anomaly detection techniques in the context of time series and discuss their strengths and drawbacks. Finally, we discuss the technical challenges and potential future directions for possible improvements in this research field.
2023-01-31
ArXiv (prépublication)
doi.org
arxiv.org