Cooperation between temporal convolutional networks (TCN) and graph
convolutional networks (GCN) as a processing module has shown promising results
in skeleton-based video anomaly detection (SVAD). However, to maintain a
lightweight model with low computational and storage complexity, shallow GCN
and TCN blocks are constrained by small receptive fields and a lack of
cross-dimension interaction capture. To tackle this limitation, we propose a
lightweight module called the Dual Attention Module (DAM) for capturing
cross-dimension interaction relationships in spatio-temporal skeletal data. It
employs the frame attention mechanism to identify the most significant frames
and the skeleton attention mechanism to capture broader relationships across
fixed partitions with minimal parameters and flops. Furthermore, the proposed
Dual Attention Normalizing Flow (DA-Flow) integrates the DAM as a
post-processing unit after GCN within the normalizing flow framework.
Simulations show that the proposed model is robust against noise and negative
samples. Experimental results show that DA-Flow reaches competitive or better
performance than the existing state-of-the-art (SOTA) methods in terms of the
micro AUC metric with the fewest number of parameters. Moreover, we found that
even without training, simply using random projection without dimensionality
reduction on skeleton data enables substantial anomaly detection capabilities.

通过引入轻量级模块的双重注意力机制 (Dual Attention Module, DAM) 以捕捉时空骨骼数据中的跨维度交互关系，以及在正常化流框架内将其作为后处理单元集成，我们提出了一种协同使用时序卷积网络 (TCN) 和图卷积网络 (GCN) 的方法在视频异常检测中取得有竞争力的性能。

DA-Flow: 面向基于骨架的视频异常检测的双重注意力标准化流

DA-Flow: Dual Attention Normalizing Flow for Skeleton-based Video  Anomaly Detection

Predicting multivariate time series is crucial, demanding precise modeling of
intricate patterns, including inter-series dependencies and intra-series
variations. Distinctive trend characteristics in each time series pose
challenges, and existing methods, relying on basic moving average kernels, may
struggle with the non-linear structure and complex trends in real-world data.
Given that, we introduce a learnable decomposition strategy to capture dynamic
trend information more reasonably. Additionally, we propose a dual attention
module tailored to capture inter-series dependencies and intra-series
variations simultaneously for better time series forecasting, which is
implemented by channel-wise self-attention and autoregressive self-attention.
To evaluate the effectiveness of our method, we conducted experiments across
eight open-source datasets and compared it with the state-of-the-art methods.
Through the comparison results, our Leddam (LEarnable Decomposition and Dual
Attention Module) not only demonstrates significant advancements in predictive
performance, but also the proposed decomposition strategy can be plugged into
other methods with a large performance-boosting, from 11.87% to 48.56% MSE
error degradation.

通过引入可学习的分解策略和双重注意力模块，该研究论文在多元时间序列预测方面取得了显著进展，并且提出的分解策略可以插入其他方法，大大提升了性能，MSE 误差降低从 11.87% 到 48.56%。