Due to the rare occurrence of anomalous events, a typical approach to anomaly
detection is to train an autoencoder (AE) with normal data only so that it
learns the patterns or representations of the normal training data. At test
time, the trained AE is expected to well reconstruct normal but to poorly
reconstruct anomalous data. However, contrary to the expectation, anomalous
data is often well reconstructed as well. In order to further separate the
reconstruction quality between normal and anomalous data, we propose creating
pseudo anomalies from learned adaptive noise by exploiting the aforementioned
weakness of AE, i.e., reconstructing anomalies too well. The generated noise is
added to the normal data to create pseudo anomalies. Extensive experiments on
Ped2, Avenue, ShanghaiTech, CIFAR-10, and KDDCUP datasets demonstrate the
effectiveness and generic applicability of our approach in improving the
discriminative capability of AEs for anomaly detection.

通过利用自编码器学习的自适应噪声产生伪异常数据，以增强自编码器在异常检测中的判别能力。

利用自编码器的弱点生成伪异常数据

Exploiting Autoencoder's Weakness to Generate Pseudo Anomalies

Most models for weakly supervised video anomaly detection (WS-VAD) rely on
multiple instance learning, aiming to distinguish normal and abnormal snippets
without specifying the type of anomaly. The ambiguous nature of anomaly
definitions across contexts introduces bias in detecting abnormal and normal
snippets within the abnormal bag. Taking the first step to show the model why
it is anomalous, a novel framework is proposed to guide the learning of
suspected anomalies from event prompts. Given a textual prompt dictionary of
potential anomaly events and the captions generated from anomaly videos, the
semantic anomaly similarity between them could be calculated to identify the
suspected anomalous events for each video snippet. It enables a new
multi-prompt learning process to constrain the visual-semantic features across
all videos, as well as provides a new way to label pseudo anomalies for
self-training. To demonstrate effectiveness, comprehensive experiments and
detailed ablation studies are conducted on four datasets, namely XD-Violence,
UCF-Crime, TAD, and ShanghaiTech. Our proposed model outperforms most
state-of-the-art methods in terms of AP or AUC (82.6\%, 87.7\%, 93.1\%, and
97.4\%). Furthermore, it shows promising performance in open-set and
cross-dataset cases.

提出了一种用于弱监督视频异常检测的新颖框架，通过计算潜在异常事件的文本提示词典与异常视频生成的字幕之间的语义异常相似性，标识每个视频片段的疑似异常事件，并通过多提示学习过程约束所有视频的视觉 - 语义特征，提供了一种为自训练标记伪异常的新方法。通过全面实验和详细消融研究，在四个数据集（XD-Violence、UCF-Crime、TAD、ShanghaiTech）上表现出优于大多数最先进方法的性能（82.6%，87.7%，93.1% 和 97.4%），并在开放集和跨数据集情况下展示了有希望的性能。

从事件提示中学习视频异常检测中的疑似异常

Learn Suspected Anomalies from Event Prompts for Video Anomaly Detection

Video anomaly detection is often seen as one-class classification (OCC)
problem due to the limited availability of anomaly examples. Typically, to
tackle this problem, an autoencoder (AE) is trained to reconstruct the input
with training set consisting only of normal data. At test time, the AE is then
expected to well reconstruct the normal data while poorly reconstructing the
anomalous data. However, several studies have shown that, even with only normal
data training, AEs can often start reconstructing anomalies as well which
depletes the anomaly detection performance. To mitigate this problem, we
propose a novel methodology to train AEs with the objective of reconstructing
only normal data, regardless of the input (i.e., normal or abnormal). Since no
real anomalies are available in the OCC settings, the training is assisted by
pseudo anomalies that are generated by manipulating normal data to simulate the
out-of-normal-data distribution. We additionally propose two ways to generate
pseudo anomalies: patch and skip frame based. Extensive experiments on three
challenging video anomaly datasets demonstrate the effectiveness of our method
in improving conventional AEs, achieving state-of-the-art performance.

本文提出了一种新的方法，通过训练自编码器（AE）模型重建正常数据，并使用生成的伪异常数据模拟数据分布，以有效改善基于 AE 的视频异常检测方法。在三个具有挑战性的视频异常检测数据集上的实验结果表明了该方法的有效性和优于传统方法的性能。