Time series anomaly detection (TSAD) is an essential problem faced in several fields, e.g., fault detection, fraud detection, and intrusion detection, etc. Although TSAD is a crucial problem in anomaly detection, few solutions in anomaly detection are suitable for it at present. Recently, some researchers use GAN-based methods such as TAnoGAN and TadGAN to solve TSAD problem. However, problems such as model collapse, low generalization capability and poor accuracy still exist. In this article, we proposed a Dilated Convolutional Transformer-based GAN (DCT-GAN) to enhance accuracy and improve generalization capability of the model. Specifically, DCT-GAN utilize several generators and a single discriminator to alleviate the mode collapse problem. Each generator consists of a dilated convolutional neural network and a Transformer block to obtain fine-grained and coarse-grained information of the time series, which is a useful component to improve generalization capability. We also use weight-based mechanism to balance these generators. Experiments verify the effectiveness of our method and each part of DCT-GAN.

Visible–infrared object detection aims to improve the detector performance by fusing the complementarity of visible and infrared images. However, most existing methods only use local intramodality information to enhance the feature representation while ignoring the efficient latent interaction of long-range dependence between different modalities, which leads to unsatisfactory detection performance under complex scenes. To solve these problems, we propose a feature-enhanced long-range attention fusion network (LRAF-Net), which improves detection performance by fusing the long-range dependence of the enhanced visible and infrared features. First, a two-stream CSPDarknet53 network is used to extract the deep features from visible and infrared images, in which a novel data augmentation (DA) method is designed to reduce the bias toward a single modality through asymmetric complementary masks. Then, we propose a cross-feature enhancement (CFE) module to improve the intramodality feature representation by exploiting the discrepancy between visible and infrared images. Next, we propose a long-range dependence fusion (LDF) module to fuse the enhanced features by associating the positional encoding of multimodality features. Finally, the fused features are fed into a detection head to obtain the final detection results. Experiments on several public datasets, i.e., VEDAI, FLIR, and LLVIP, show that the proposed method obtains state-of-the-art performance compared with other methods.

Feature-level fusion methods have demonstrated superior performance for visible- infrared object detection due to the deep exploration of visible and infrared features. However, most existing feature-level fusion methods utilize multiple convolutional layers with fixed parameters to extract bimodal features, leading to low adaptivity to diverse data distributions. This paper proposes a Cross-scale Dynamic Convolution-driven YOLO Fusion (CDC-YOLOFusion) network, which introduces a novel Cross-scale Dynamic Convolution Fusion (CDCF) module to adaptively extract and fuse bimodal features concerning on data distribution. Technically, CDC-YOLOFusion first designs a novel data augmentation strategy “Cross-modal Data Swapping” (CDS) to exchange local regions between visible and infrared images, effectively capturing cross-modal correlations within local regions. Building on this, the proposed CDCF utilizes cross-scale enhanced features to assist dynamic convolution prediction by introducing a disparity attention mask, which emphasizes the extraction of disparate features between two modalities. Our CDCF is effectively guided by a novel cross-modal kernel interaction loss, aiming the learned kernels to simultaneously focus on common salient features and unique features of each modality for comprehensive feature generation. Extensive experiments on three representative detection datasets demonstrate that CDCF can be easily plugined into the existing pipelines, obtaining consistent performance improvements. Moreover, our approach yields SOTA performance with about 2% to 3% mAP improvements as compared to the state-of-the-art methods. The code has been published in https://github.com/Morph-3800/CDC_YOLOFusion .