We introduce Dynamic Distinction Learning (DDL) for Video Anomaly Detection,
a novel video anomaly detection methodology that combines pseudo-anomalies,
dynamic anomaly weighting, and a distinction loss function to improve detection
accuracy. By training on pseudo-anomalies, our approach adapts to the
variability of normal and anomalous behaviors without fixed anomaly thresholds.
Our model showcases superior performance on the Ped2, Avenue and ShanghaiTech
datasets, where individual models are tailored for each scene. These
achievements highlight DDL's effectiveness in advancing anomaly detection,
offering a scalable and adaptable solution for video surveillance challenges.

介绍了动态差异学习（DDL）用于视频异常检测的研究方法，该方法结合了伪异常、动态异常加权和差异损失函数，提高了检测精度，能够适应正常和异常行为的变异性，并在 Ped2、Avenue 和 ShanghaiTech 数据集上展示出卓越性能，为视频监控挑战提供了可扩展和适应性强的解决方案。

动态区分学习：自适应伪异常用于视频异常检测

Dynamic Distinction Learning: Adaptive Pseudo Anomalies for Video  Anomaly Detection

Video Anomaly Detection (VAD) is an open-set recognition task, which is
usually formulated as a one-class classification (OCC) problem, where training
data is comprised of videos with normal instances while test data contains both
normal and anomalous instances. Recent works have investigated the creation of
pseudo-anomalies (PAs) using only the normal data and making strong assumptions
about real-world anomalies with regards to abnormality of objects and speed of
motion to inject prior information about anomalies in an autoencoder (AE) based
reconstruction model during training. This work proposes a novel method for
generating generic spatio-temporal PAs by inpainting a masked out region of an
image using a pre-trained Latent Diffusion Model and further perturbing the
optical flow using mixup to emulate spatio-temporal distortions in the data. In
addition, we present a simple unified framework to detect real-world anomalies
under the OCC setting by learning three types of anomaly indicators, namely
reconstruction quality, temporal irregularity and semantic inconsistency.
Extensive experiments on four VAD benchmark datasets namely Ped2, Avenue,
ShanghaiTech and UBnormal demonstrate that our method performs on par with
other existing state-of-the-art PAs generation and reconstruction based methods
under the OCC setting. Our analysis also examines the transferability and
generalisation of PAs across these datasets, offering valuable insights by
identifying real-world anomalies through PAs.

通过使用预先训练的潜在扩散模型修复被屏蔽区域图像，并进一步通过混合来扰动光流以产生时间空间扭曲，提出了一种新的生成通用时空伪异常的方法。此外，通过学习重建质量、时间不规则性和语义不一致性等三种异常指标的简单统一框架，在开放集识别任务下检测现实世界的异常。在 Ped2、Avenue、ShanghaiTech 和 UBnormal 四个 VAD 基准数据集上进行了大量实验，证明我们的方法在 OCC 设置下的性能与其他现有的基于伪异常生成和重建的方法相当。同时我们的分析还检验了伪异常在这些数据集之间的可转移性和普适性，通过识别伪异常从而发现现实世界的异常提供了有价值的见解。

基于时空伪异常生成的视频异常检测：一种统一方法

Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation :  A Unified Approach

Anomaly detection (AD) is essential in identifying rare and often critical
events in complex systems, finding applications in fields such as network
intrusion detection, financial fraud detection, and fault detection in
infrastructure and industrial systems. While AD is typically treated as an
unsupervised learning task due to the high cost of label annotation, it is more
practical to assume access to a small set of labeled anomaly samples from
domain experts, as is the case for semi-supervised anomaly detection.
Semi-supervised and supervised approaches can leverage such labeled data,
resulting in improved performance. In this paper, rather than proposing a new
semi-supervised or supervised approach for AD, we introduce a novel algorithm
for generating additional pseudo-anomalies on the basis of the limited labeled
anomalies and a large volume of unlabeled data. This serves as an augmentation
to facilitate the detection of new anomalies. Our proposed algorithm, named
Nearest Neighbor Gaussian Mixup (NNG-Mix), efficiently integrates information
from both labeled and unlabeled data to generate pseudo-anomalies. We compare
the performance of this novel algorithm with commonly applied augmentation
techniques, such as Mixup and Cutout. We evaluate NNG-Mix by training various
existing semi-supervised and supervised anomaly detection algorithms on the
original training data along with the generated pseudo-anomalies. Through
extensive experiments on 57 benchmark datasets in ADBench, reflecting different
data types, we demonstrate that NNG-Mix outperforms other data augmentation
methods. It yields significant performance improvements compared to the
baselines trained exclusively on the original training data. Notably, NNG-Mix
yields up to 16.4%, 8.8%, and 8.0% improvements on Classical, CV, and NLP
datasets in ADBench. Our source code will be available at
this https URL

提出了一种名为最近邻高斯混合 (NNG-Mix) 的新算法，通过整合有标签和无标签数据来生成伪异常样本，用于改进半监督和监督异常检测算法，与常用的数据增强方法相比，在 57 个数据集上取得显著的性能提升，对于 ADBench 中的经典、计算机视觉和自然语言处理数据集，最多可提高 16.4%、8.8% 和 8.0%。