Increasing scene-awareness is a key challenge in video anomaly detection
(VAD). In this work, we propose a hierarchical semantic contrast (HSC) method
to learn a scene-aware VAD model from normal videos. We first incorporate
foreground object and background scene features with high-level semantics by
taking advantage of pre-trained video parsing models. Then, building upon the
autoencoder-based reconstruction framework, we introduce both scene-level and
object-level contrastive learning to enforce the encoded latent features to be
compact within the same semantic classes while being separable across different
classes. This hierarchical semantic contrast strategy helps to deal with the
diversity of normal patterns and also increases their discrimination ability.
Moreover, for the sake of tackling rare normal activities, we design a
skeleton-based motion augmentation to increase samples and refine the model
further. Extensive experiments on three public datasets and scene-dependent
mixture datasets validate the effectiveness of our proposed method.

本文提出了一种基于层次语义对比的视频异常检测模型，结合了前景对象和背景场景特征，通过自动编码器框架增强编码的潜在特征，同时使用对象级和场景级对比学习，增强模型的判别能力，并通过基于骨架的运动增强来进一步处理少见的正常活动。经过在三个常规和场景相关混合数据集上的实验证明了这种方法的有效性。

基于层次化语义对场景感知视频异常检测的对比研究

Hierarchical Semantic Contrast for Scene-aware Video Anomaly Detection

Knowing the degree of semantic contrast between words has widespread
application in natural language processing, including machine translation,
information retrieval, and dialogue systems. Manually-created lexicons focus on
opposites, such as {\rm hot} and {\rm cold}. Opposites are of many kinds such
as antipodals, complementaries, and gradable. However, existing lexicons often
do not classify opposites into the different kinds. They also do not explicitly
list word pairs that are not opposites but yet have some degree of contrast in
meaning, such as {\rm warm} and {\rm cold} or {\rm tropical} and {\rm
freezing}. We propose an automatic method to identify contrasting word pairs
that is based on the hypothesis that if a pair of words, $A$ and $B$, are
contrasting, then there is a pair of opposites, $C$ and $D$, such that $A$ and
$C$ are strongly related and $B$ and $D$ are strongly related. (For example,
there exists the pair of opposites {\rm hot} and {\rm cold} such that {\rm
tropical} is related to {\rm hot,} and {\rm freezing} is related to {\rm
cold}.) We will call this the contrast hypothesis. We begin with a large
crowdsourcing experiment to determine the amount of human agreement on the
concept of oppositeness and its different kinds. In the process, we flesh out
key features of different kinds of opposites. We then present an automatic and
empirical measure of lexical contrast that relies on the contrast hypothesis,
corpus statistics, and the structure of a {\it Roget}-like thesaurus. We show
that the proposed measure of lexical contrast obtains high precision and large
coverage, outperforming existing methods.

提出了一种新的自动方法，基于语言学中的反义词，并通过语料库的统计结构来识别具有反义但不同种类的词对以及识别单词对之间的语义差异度。