Anomaly detection is a research hotspot in the field of object detection, aiming to construct models using normal samples to detect anomalies. The challenge of this task is the extreme imbalance of the dataset, and training models based on such datasets do not have good generalization ability. In order to solve the problem of low abnormal data affecting detection performance, we propose an asymmetric self-coding network based on knowledge distillation, combined with anomaly detection algorithms. Our method only uses normal samples for training, allowing the encoder to learn the distribution of normal samples in deep space. We use the decoder to reconstruct and restore deep features, outputting a generated graph of the corresponding samples. By forming an asymmetric structure with a lightweight decoder and encoder, the problem of reconstruction error failure is solved. The knowledge distillation algorithm is combined to train the network, using the pretrained encoding network as the teacher network and guiding the reconstruction of the asymmetric decoding network as the student network. A new multi-scale loss function is designed, which is composed of pixel level and global direction loss function. Experiments show that the average AUC of each category of MVTec AD dataset in our method is significantly higher than other anomaly detection methods. Especially when knowledge distillation strategy is used in reconstruction methods, the average AUC of our method is about 2 points higher than the highest MKD network.
In recent years, face restoration methods based on deep learning with or without GAN prior have two main problems: retaining less identity information of the original input image and insufficient utilization of facial structure information. In order to solve the mentioned problems, we propose an encoder-decoder architecture face restoration network with style modulation called EDSM. First, skip connection and channel attention module are added to the basic network and a lightweight style modulation module is introduced to make full use of the global and local information extracted from the low-resolution (LR) face image. Meanwhile, identity loss is introduced to preserve identity information and a multi-scale discriminator is added to constitute the EDSM-plus network. Experiments have shown that the proposed EDSM and EDSM-plus have good face restoration performance in the Helen dataset.
As an important part of automobile safety system, distracted driving behavior recognition has important research value. By analyzing the limitations and difficulties of the existing distraction driving recognition methods, this paper proposes a two-stage dual-channel recognition network. In the first stage, the Alphapose key point detection network based on SF3D data set pre-training is used to obtain the driver 's key point information, and the key area heat map is generated based on the Gaussian heat map. It is combined with the original image to form the two-channel input of the second stage. The fusion feature is generated by the feature fusion module based on feature concatenation, and it is used as the input of the second stage ResNet-50 backbone recognition network for recognition. Finally, in order to enhance the recognition effect, this paper introduces spatial and channel attention mechanisms to enhance the learning of interest features. And comparison and ablation experiments are designed for the proposed method. Compared with the benchmark network model, the proposed method improves 2.6 points, which verifies the effectiveness of the algorithm.
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