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

Motion in-betweening (MIB) is a process of generating intermediate skeletal
movement between the given start and target poses while preserving the
naturalness of the motion, such as periodic footstep motion while walking.
Although state-of-the-art MIB methods are capable of producing plausible
motions given sparse key-poses, they often lack the controllability to generate
motions satisfying the semantic contexts required in practical applications. We
focus on the method that can handle pose or semantic conditioned MIB tasks
using a unified model. We also present a motion augmentation method to improve
the quality of pose-conditioned motion generation via defining a distribution
over smooth trajectories. Our proposed method outperforms the existing
state-of-the-art MIB method in pose prediction errors while providing
additional controllability.

该论文提出了一种方法 ——Motion in-betweening (MIB)，它可以在保持运动自然性的同时，生成开始和目标姿势之间的中间骨架运动；该方法还可以处理在姿态或语义条件下的 MIB 任务，并提供增强运动生成质量的运动增强方法。该方法在姿态预测误差方面优于现有的 MIB 方法，并提供了更多可控性。