Video Anomaly Detection (VAD) is a challenging task due to the variability of anomalous events and the limited availability of labeled data. Under the Weakly-Supervised VAD (WSVAD) paradigm, only video-level labels are provided during training, while predictions are made at the frame level. Although state-of-the-art models perform well on simple anomalies (e.g., explosions), they struggle with complex real-world events (e.g., shoplifting). This difficulty stems from two key issues: (1) the inability of current models to address the diversity of anomaly types, as they process all categories with a shared model, overlooking category-specific features; and (2) the weak supervision signal, which lacks precise temporal information, limiting the ability to capture nuanced anomalous patterns blended with normal events. To address these challenges, we propose Gaussian Splatting-guided Mixture of Experts (GS-MoE), a novel framework that employs a set of expert models, each specialized in capturing specific anomaly types. These experts are guided by a temporal Gaussian splatting loss, enabling the model to leverage temporal consistency and enhance weak supervision. The Gaussian splatting approach encourages a more precise and comprehensive representation of anomalies by focusing on temporal segments most likely to contain abnormal events. The predictions from these specialized experts are integrated through a mixture-of-experts mechanism to model complex relationships across diverse anomaly patterns. Our approach achieves state-of-the-art performance, with a 91.58% AUC on the UCF-Crime dataset, and demonstrates superior results on XD-Violence and MSAD datasets. By leveraging category-specific expertise and temporal guidance, GS-MoE sets a new benchmark for VAD under weak supervision.

The Compact Muon Solenoid (CMS) experiment studies these collisions to uncover potential Beyond Standard Model (BSM) physics and precisely measure rare Standard Model (SM) processes [2]. While the high collision rate at the LHC increases the probability of producing and detecting rare processes, the nearly 100 million channels of the CMS detector also generate an enormous amount of data [10, 14]. Only a small fraction of the 40 MHz proton-proton collision events--around 1,000 per second--can be stored for detailed offline analysis. To meet this stringent data reduction, events are selected using a two-tiered trigger system. The first level (L1), composed of custom hardware processors built with field-programmable gate arrays (FPGAs), uses information from the calorimeters and muon detectors to select events at a rate of around 100 kHz within a fixed latency of 4 [14]. The second level, the high-level trigger (HLT), consists of a processor farm running optimized event reconstruction software, reducing the rate to around 1 kHz before storage [10].

Video anomaly understanding (VAU) aims to automatically comprehend unusual occurrences in videos, thereby enabling various applications such as traffic surveillance and industrial manufacturing. While existing VAU benchmarks primarily concentrate on anomaly detection and localization, our focus is on more practicality, prompting us to raise the following crucial questions: "what anomaly occurred?", "why did it happen?", and "how severe is this abnormal event?". In pursuit of these answers, we present a comprehensive benchmark for Causation Understanding of Video Anomaly (CUVA). Specifically, each instance of the proposed benchmark involves three sets of human annotations to indicate the "what", "why" and "how" of an anomaly, including 1) anomaly type, start and end times, and event descriptions, 2) natural language explanations for the cause of an anomaly, and 3) free text reflecting the effect of the abnormality. In addition, we also introduce MMEval, a novel evaluation metric designed to better align with human preferences for CUVA, facilitating the measurement of existing LLMs in comprehending the underlying cause and corresponding effect of video anomalies. Finally, we propose a novel prompt-based method that can serve as a baseline approach for the challenging CUVA. We conduct extensive experiments to show the superiority of our evaluation metric and the prompt-based approach. Our code and dataset are available at https://github.com/fesvhtr/CUVA.

Multivariate Time Series (MVTS) anomaly detection is a long-standing and challenging research topic that has attracted tremendous research effort from both industry and academia recently. However, a careful study of the literature makes us realize that 1) the community is active but not as organized as other sibling machine learning communities such as Computer Vision (CV) and Natural Language Processing (NLP), and 2) most proposed solutions are evaluated using either inappropriate or highly flawed protocols, with an apparent lack of scientific foundation. So flawed is one very popular protocol, the so-called point-adjust protocol, that a random guess can be shown to systematically outperform all algorithms developed so far. In this paper, we review and evaluate many recent algorithms using more robust protocols and discuss how a normally good protocol may have weaknesses in the context of MVTS anomaly detection and how to mitigate them. We also share our concerns about benchmark datasets, experiment design and evaluation methodology we observe in many works. Furthermore, we propose a simple, yet challenging, baseline based on Principal Components Analysis (PCA) that surprisingly outperforms many recent Deep Learning (DL) based approaches on popular benchmark datasets. The main objective of this work is to stimulate more effort towards important aspects of the research such as data, experiment design, evaluation methodology and result interpretability, instead of putting the highest weight on the design of increasingly more complex and "fancier" algorithms.

Anomaly detection research works generally propose algorithms or end-to-end systems that are designed to automatically discover outliers in a dataset or a stream. While literature abounds concerning algorithms or the definition of metrics for better evaluation, the quality of the ground truth against which they are evaluated is seldom questioned. In this paper, we present a systematic analysis of available public (and additionally our private) ground truth for anomaly detection in the context of network environments, where data is intrinsically temporal, multivariate and, in particular, exhibits spatial properties, which, to the best of our knowledge, we are the first to explore. Our analysis reveals that, while anomalies are, by definition, temporally rare events, their spatial characterization clearly shows some type of anomalies are significantly more popular than others. We find that simple clustering can reduce the need for human labeling by a factor of 2x-10x, that we are first to quantitatively analyze in the wild.

As the default protocol for exchanging routing reachability information on the Internet, the abnormal behavior in traffic of Border Gateway Protocols (BGP) is closely related to Internet anomaly events. The BGP anomalous detection model ensures stable routing services on the Internet through its real-time monitoring and alerting capabilities. Previous studies either focused on the feature selection problem or the memory characteristic in data, while ignoring the relationship between features and the precise time correlation in feature (whether it's long or short term dependence). In this paper, we propose a multi-view model for capturing anomalous behaviors from BGP update traffic, in which Seasonal and Trend decomposition using Loess (STL) method is used to reduce the noise in the original time-series data, and Graph Attention Network (GAT) is used to discover feature relationships and time correlations in feature, respectively. Our results outperform the state-of-the-art methods at the anomaly detection task, with the average F1 score up to 96.3% and 93.2% on the balanced and imbalanced datasets respectively. Meanwhile, our model can be extended to classify multiple anomalous and to detect unknown events.

Abstract--There are numerous methods for detecting anomalies in time series, but that is only the first step to understanding them. We strive to exceed this by explaining those anomalies. Thus we develop a novel attribution scheme for multivariate time series relying on counterfactual reasoning. We aim to answer the counterfactual question of would the anomalous event have occurred if the subset of the involved variables had been more similarly distributed to the data outside of the anomalous interval. By determining which variables yield the lowest anomaly score Finding causes of extreme weather events, power outages after the replacement, we can conclude that the subset of and abnormal fluctuations in financial data can be of crucial variables in question was the reason why the anomaly had importance for their understanding and taking precautionary occurred. We propose a novel anomaly attribution scheme Our attribution method can be applied to any multivariate to analyze anomalous intervals of multivariate temporal and time series data regardless of potential outliers and missing spatio-temporal data and attribute those anomalies to a set of values.

Most recent studies on detecting and localizing temporal anomalies have mainly employed deep neural networks to learn the normal patterns of temporal data in an unsupervised manner. Unlike them, the goal of our work is to fully utilize instance-level (or weak) anomaly labels, which only indicate whether any anomalous events occurred or not in each instance of temporal data. In this paper, we present WETAS, a novel framework that effectively identifies anomalous temporal segments (i.e., consecutive time points) in an input instance. WETAS learns discriminative features from the instance-level labels so that it infers the sequential order of normal and anomalous segments within each instance, which can be used as a rough segmentation mask. Based on the dynamic time warping (DTW) alignment between the input instance and its segmentation mask, WETAS obtains the result of temporal segmentation, and simultaneously, it further enhances itself by using the mask as additional supervision. Our experiments show that WETAS considerably outperforms other baselines in terms of the localization of temporal anomalies, and also it provides more informative results than point-level detection methods.

Due to its relevance in intelligent transportation systems, anomaly detection in traffic videos has recently received much interest. It remains a difficult problem due to a variety of factors influencing the video quality of a real-time traffic feed, such as temperature, perspective, lighting conditions, and so on. Even though state-of-the-art methods perform well on the available benchmark datasets, they need a large amount of external training data as well as substantial computational resources. In this paper, we propose an efficient approach for a video anomaly detection system which is capable of running at the edge devices, e.g., on a roadside camera. The proposed approach comprises a pre-processing module that detects changes in the scene and removes the corrupted frames, a two-stage background modelling module and a two-stage object detector. Finally, a backtracking anomaly detection algorithm computes a similarity statistic and decides on the onset time of the anomaly. We also propose a sequential change detection algorithm that can quickly adapt to a new scene and detect changes in the similarity statistic. Experimental results on the Track 4 test set of the 2021 AI City Challenge show the efficacy of the proposed framework as we achieve an F1-score of 0.9157 along with 8.4027 root mean square error (RMSE) and are ranked fourth in the competition.

If you are perplexed, befuddled and bewildered about reports of unidentified aerial phenomena (UAP) and possible visitations of alien spacecraft, you can take action with do-it-yourself sky-monitoring gear. Given the low cost and high capability of today's consumer-grade technology, you too can be at the ready to document out-of-the-ordinary events. Enter the world of Sky Hub, a global network of smart sensors designed to snag digital signatures of anomalous events. These advancements can be harnessed to probe the ongoing, baffling behavior of UAP and unidentified flying objects (UFOs) reportedly crisscrossing the skies. The mission of Sky Hub is clearcut: Connect a network of civilian-owned sensor arrays, use machine learning to catalogue anomalous events, and share this data with researchers.