Cross-Dimension Attentive Feature Fusion Network for Unsupervised Time-Series Anomaly Detection

Time series anomaly detection is crucial in various industrial applications to identify unusual behaviors within the time series data.Due to the challenges associated with annotating anomaly events,time series reconstruction has become a prevalent approach for unsupervised anomaly detection.However,effectively learning representations and achieving accurate detection results remain challenging due to the intricate temporal patterns and dependencies in real-world time series.In this paper,we propose a cross-dimension attentive feature fusion network for time series anomaly detection,referred to as CAFFN.Specifically,a series and feature mixing block is introduced to learn representations in 1D space.Additionally,a fast Fourier transform is employed to convert the time series into 2D space,providing the capability for 2D feature extraction.Finally,a cross-dimension attentive feature fusion mechanism is designed that adaptively integrates features across different dimensions for anomaly detection.Experimental results on real-world time series datasets demonstrate that CAFFN performs better than other competing methods in time series anomaly detection.

Global-Attention-Based Neural Networks for Vision Language Intelligence

In this paper,we develop a novel global-attentionbased neural network(GANN)for vision language intelligence,specifically,image captioning(language description of a given image).As many previous works,the encoder-decoder framework is adopted in our proposed model,in which the encoder is responsible for encoding the region proposal features and extracting global caption feature based on a specially designed module of predicting the caption objects,and the decoder generates captions by taking the obtained global caption feature along with the encoded visual features as inputs for each attention head of the decoder layer.The global caption feature is introduced for the purpose of exploring the latent contributions of region proposals for image captioning,and further helping the decoder better focus on the most relevant proposals so as to extract more accurate visual feature in each time step of caption generation.Our GANN is implemented by incorporating the global caption feature into the attention weight calculation phase in the word predication process in each head of the decoder layer.In our experiments,we qualitatively analyzed the proposed model,and quantitatively evaluated several state-of-the-art schemes with GANN on the MS-COCO dataset.Experimental results demonstrate the effectiveness of the proposed global attention mechanism for image captioning.