With a focus on abnormal events contained within untrimmed videos, there is increasing interest among researchers in video anomaly detection. Among different video anomaly detection scenarios, weakly-supervised video anomaly detection poses a significant challenge as it lacks frame-wise labels during the training stage, only relying on video-level labels as coarse supervision. Previous methods have made attempts to either learn discriminative features in an end-to-end manner or employ a two-stage self-training strategy to generate snippet-level pseudo labels. However, both approaches have certain limitations. The former tends to overlook informative features at the snippet level, while the latter can be susceptible to noises. In this paper, we propose an Anomalous Attention mechanism for weakly-supervised anomaly detection to tackle the aforementioned problems. Our approach takes into account snippet-level encoded features without the supervision of pseudo labels. Specifically, our approach first generates snippet-level anomalous attention and then feeds it together with original anomaly scores into a Multi-branch Supervision Module. The module learns different areas of the video, including areas that are challenging to detect, and also assists the attention optimization. Experiments on benchmark datasets XD-Violence and UCF-Crime verify the effectiveness of our method. Besides, thanks to the proposed snippet-level attention, we obtain a more precise anomaly localization.

Digital watermarking is an effective covert communication technique in the military field, which can solve the problems in traditional encryption. In general, the Spread Spectrum (SS) algorithm can well satisfy the requirements of concealment and anti-interference in the covert communication process. However, it has a natural defect in resisting desynchronization attacks. In this paper, we propose a Robust Feature Points Scheme (RFPS) against desynchronization attacks by calculating the maximum response value of the second-order derivative of the original audio signal. The maximum response value will be used as a feature point and the audio segments centering at the detected feature points are extracted for both watermark embedding and extraction process. This study mainly focuses on cropping attacks and pitch invariant Time-Scale Modification (TSM) attacks. The experimental results show that under non-uniform cropping attacks, the proposed method can extract the watermark information without any bit error rate. Even in the case of the pitch-invariant TSM attacks, the proposed scheme has great improvement compared with the existing works.

The generation-based data augmentation method can overcome the challenge caused by the imbalance of medical image data to a certain extent. However, most of the current research focus on images with unified structure which are easy to learn. What is different is that ultrasound images are structurally inadequate, making it difficult for the structure to be captured by the generative network, resulting in the generated image lacks structural legitimacy. Therefore, a Progressive Generative Adversarial Method for Structurally Inadequate Medical Image Data Augmentation is proposed in this paper, including a network and a strategy. Our Progressive Texture Generative Adversarial Network alleviates the adverse effect of completely truncating the reconstruction of structure and texture during the generation process and enhances the implicit association between structure and texture. The Image Data Augmentation Strategy based on Mask-Reconstruction overcomes data imbalance from a novel perspective, maintains the legitimacy of the structure in the generated data, as well as increases the diversity of disease data interpretably. The experiments prove the effectiveness of our method on data augmentation and image reconstruction on Structurally Inadequate Medical Image both qualitatively and quantitatively. Finally, the weakly supervised segmentation of the lesion is the additional contribution of our method.

Single image dehazing has always been a challenging problem in the field of computer vision. Traditional image defogging methods use manual features. With the development of artificial intelligence, the defogging method based on deep learning has developed rapidly. In this paper, we propose a novel image defogging approach called NIN-DehazeNet for single image. This method estimates the transmission map by NIN-DehazeNet combining Network-in-Network with MSCNN(Single Image Dehazing via Multi-Scale Convolutional Neural Networks). In the test stage, we estimate the transmission map of the input hazy image based on the trained model, and then generate the dehazed image using the estimated atmospheric light and computed transmission map. Extensive experiments have shown that the proposed algorithm overperformance traditional methods.

Due to the fast transmission speed and severe health damage, COVID-19 has attracted global attention. Early diagnosis and isolation are effective and imperative strategies for epidemic prevention and control. Most diagnostic methods for the COVID-19 is based on nucleic acid testing (NAT), which is expensive and time-consuming. To build an efficient and valid alternative of NAT, this article investigates the feasibility of employing computed tomography images of lungs as the diagnostic signals. Unlike normal lungs, parts of the lungs infected with the COVID-19 developed lesions, ground-glass opacity, and bronchiectasis became apparent. Through a public dataset, in this article, we propose an advanced residual learning diagnosis detection (RLDD) scheme for the COVID-19 technique, which is designed to distinguish positive COVID-19 cases from heterogeneous lung images. Besides the advantage of high diagnosis effectiveness, the designed residual-based COVID-19 detection network can efficiently extract the lung features through small COVID-19 samples, which removes the pretraining requirement on other medical datasets. In the test set, we achieve an accuracy of 91.33%, a precision of 91.30%, and a recall of 90%. For the batch of 150 samples, the assessment time is only 4.7 s. Therefore, RLDD can be integrated into the application programming interface and embedded into the medical instrument to improve the detection efficiency of COVID-19.