The goal of the feature reconstruction network based on an autoencoder in the training phase is to force the network to reconstruct the input features well. The network tends to learn shortcuts of “identity mapping,” which leads to the network outputting abnormal features as they are in the inference phase. As such, the abnormal features based on reconstruction error cannot be distinguished from normal features, significantly limiting the detection performance of such methods. To address this issue, we propose a feature transformation reconstruction (FTR) network, which can avoid the identity mapping problem. Specifically, we use a normalizing flow model as a feature transformation (FT) network to transform input features into other forms. The training goal of the feature reconstruction (FR) network is no longer to reconstruct the input features but to reconstruct the transformed features, effectively avoiding the shortcut of learning the “identity map.” Furthermore, this paper proposes a masked convolutional attention (MCA) module, which randomly masks the input features in the training phase and reconstructs the input features in a self‐supervised manner. In the testing phase, the MCA can effectively suppress the excessive reconstruction of abnormal features and further improve anomaly detection performance. FTR achieves the scores of the area under the receiver operating characteristic curve (AUROC) at 99.5% and 97.8% on the MVTec AD and BTAD datasets, respectively, outperforming other state‐of‐the‐art methods. Moreover, FTR is faster than the existing methods, with a high speed of 137 frames per second (FPS) on a 3080ti GPU.

Cross-view vehicle Reidentification (ReID) has attracted widespread attention as an increasingly important vision task in intelligent transportation and urban surveillance. Benefiting from Convolutional Neural Network (CNN), recent studies have promoted the development of vehicle ReID by extracting discriminative local features. However, two fundamental challenges of small interclass discrepancy caused by different views and large intraclass distance caused by similar appearance still hinder the performance of cross-view vehicle ReID. In this paper, a novel View-aware Sphere Learning Network (VSLN) is proposed to alleviate the above issues while maintaining the merits of CNN-based approaches to generate view-aware sphere-based features. First, a Sphere Feature Embedding Network (SFEN) is proposed to constrain the images into hypersphere for extracting sphere features. On the other hand, this study presents a sphere similarity triple loss to help SFEN concentrate more on robust and discriminative vehicle parts. Second, since the vehicle images are usually captured from different viewpoints, this study further extends SFEN by introducing a Vehicle Viewpoint Predictor (VVP) combined with global attention mechanism to enlarge the discrepancy of interclass and shorten the distance of intraclass. Moreover, a city-scale data set, named Vehicle from Different Viewpoints, containing image-level viewpoint labels, is collected for training VVP. As a result, the proposed VLSN can achieve 96.31% Top-1 accuracy and 79.46% Top-1 accuracy on VeRi-776 and VRIC data sets, respectively. Overall, extensive experimental results on two benchmark data sets show that the proposed VSLN outperforms state-of-the-art methods.