This work tackles Weakly Supervised Anomaly detection, in which a predictor
is allowed to learn not only from normal examples but also from a few labeled
anomalies made available during training. In particular, we deal with the
localization of anomalous activities within the video stream: this is a very
challenging scenario, as training examples come only with video-level
annotations (and not frame-level). Several recent works have proposed various
regularization terms to address it i.e. by enforcing sparsity and smoothness
constraints over the weakly-learned frame-level anomaly scores. In this work,
we get inspired by recent advances within the field of self-supervised learning
and ask the model to yield the same scores for different augmentations of the
same video sequence. We show that enforcing such an alignment improves the
performance of the model on XD-Violence.

本文针对弱监督异常检测进行研究，利用自监督学习策略，通过对同一视频序列数据进行不同变换来使模型获得相同的分数，并通过实验证明这种对齐方式可以提高模型在 XD-Violence 数据集上的性能表现。

面向时间异常定位的基于一致性自监督学习

Consistency-based Self-supervised Learning for Temporal Anomaly Localization

In medical applications, weakly supervised anomaly detection methods are of
great interest, as only image-level annotations are required for training.
Current anomaly detection methods mainly rely on generative adversarial
networks or autoencoder models. Those models are often complicated to train or
have difficulties to preserve fine details in the image. We present a novel
weakly supervised anomaly detection method based on denoising diffusion
implicit models. We combine the deterministic iterative noising and denoising
scheme with classifier guidance for image-to-image translation between diseased
and healthy subjects. Our method generates very detailed anomaly maps without
the need for a complex training procedure. We evaluate our method on the
BRATS2020 dataset for brain tumor detection and the CheXpert dataset for
detecting pleural effusions.

我们提出了一种基于去噪扩散内隐模型的弱监督异常检测方法，结合分类器指导进行病态和健康主体之间的图像翻译，生成非常详细的异常地图，无需复杂的训练过程。