Weakly-Supervised and Unsupervised Video Anomaly Detection

Due to the limited availability of anomaly examples, video anomaly detection is often seen as one-class classification (OCC) problem. A popular way to tackle this problem is by utilizing an autoencoder (AE) trained only on normal data. At test time, the AE is then expected to reconstruct the normal input well while reconstructing the anomalies poorly. However, several studies show that, even with normal data only training, AEs can often start reconstructing anomalies as well which depletes their anomaly detection performance. To mitigate this, we propose a temporal pseudo anomaly synthesizer that generates fake-anomalies using only normal data. An AE is then trained to maximize the reconstruction loss on pseudo anomalies while minimizing this loss on normal data. This way, the AE is encouraged to produce distinguishable reconstructions for normal and anomalous frames. Extensive experiments and analysis on three challenging video anomaly datasets demonstrate the effectiveness of our approach to improve the basic AEs in achieving superiority against several existing state-of-the-art models.

Video anomaly detection is of great significance due to its wide applications in video surveillance. Recently, there is a trend of using a video prediction framework to tackle this problem. This kind of methods detects anomalies according to the difference between a predicted frame and its ground truth. However, existing prediction methods lack the consideration of multi-scale temporal constraints on generating future frames. Therefore, based on the prediction framework, this paper proposes a temporal enhanced anomaly detection approach, which designs a generative adversarial network with dual discriminator (frame discriminator and sequence discriminator) to predict future frames. In order to obtain more realistic predictions, other than commonly used spatial constraints and the adversarial penalty from the frame discriminator, we also consider both short-range and long-range motions to impose constraints. Specifically, for short-range motion modeling, we utilize the optical flow loss to ensure temporal continuity over two adjacent frames, while for long-range motion modeling, we design a sequence discriminator to identify fake contained sequences from real sequences, making frames more consistent with their previous consecutive frames. Experiments on three datasets, UCSD Ped1, UCSD Ped2 and Avenue, demonstrate the effectiveness of our method in terms of various evaluation criteria for video anomaly detection.

Anomaly detection in videos is the task of identifying frames from a video sequence that depict events that do not conform to expected behavior, which is an extremely challenging task due to the ambiguous and unbounded properties of anomalies. With the development of deep learning, video anomaly detection methods based on deep neural networks have made great progress. The existing methods mainly follow two routes, namely, frame reconstruction and frame prediction. Due to the powerful generalization ability of neural networks, the application of reconstruction-based methods is limited. Recently, anomaly detection methods based on prediction have achieved advanced performance. However, their performance suffers when they cannot guarantee lower prediction errors for normal events. In this paper, we propose a novel future frame prediction model based on a bidirectional retrospective generation adversarial network (BR-GAN) for anomaly detection. To predict a future frame with higher quality for normal events, first, we propose a bidirectional prediction combined with a retrospective prediction method to fully mine the bidirectional temporal information between the predicted frame and the input frame sequence. Then, the intensity and gradient loss between the predicted frame and the actual frame together with an adversarial loss are used for appearance (spatial) constraints. In addition, we propose a sequence discriminator composed of a 3-dimensional (3D) convolutional neural network to capture the long-term temporal relationships between frame sequences composed of predicted frames and input frames; this network plays a crucial role in maintaining the motion (temporal) consistency of the predicted frames for normal events. Such appearance and motion constraints further facilitate future frame prediction for normal events, and thus, the prediction network can be highly capable of distinguishing normal and abnormal patterns. Extensive experiments on benchmark datasets demonstrate that our method outperforms most existing state-of-the-art methods, validating the effectiveness of our method for anomaly detection.

Nowadays, machine learning (ML) has attained a high level of achievement in many contexts. Considering the significance of ML in medical and bioinformatics owing to its accuracy, many investigators discussed multiple solutions for developing the function of medical and bioinformatics challenges using deep learning (DL) techniques. The importance of DL in Internet of Things (IoT)-based bio- and medical informatics lies in its ability to analyze and interpret large amounts of complex and diverse data in real time, providing insights that can improve healthcare outcomes and increase efficiency in the healthcare industry. Several applications of DL in IoT-based bio- and medical informatics include diagnosis, treatment recommendation, clinical decision support, image analysis, wearable monitoring, and drug discovery. The review aims to comprehensively evaluate and synthesize the existing body of the literature on applying deep learning in the intersection of the IoT with bio- and medical informatics. In this paper, we categorized the most cutting-edge DL solutions for medical and bioinformatics issues into five categories based on the DL technique utilized: convolutional neural network, recurrent neural network, generative adversarial network, multilayer perception, and hybrid methods. A systematic literature review was applied to study each one in terms of effective properties, like the main idea, benefits, drawbacks, methods, simulation environment, and datasets. After that, cutting-edge research on DL approaches and applications for bioinformatics concerns was emphasized. In addition, several challenges that contributed to DL implementation for medical and bioinformatics have been addressed, which are predicted to motivate more studies to develop medical and bioinformatics research progressively. According to the findings, most articles are evaluated using features like accuracy, sensitivity, specificity, F-score, latency, adaptability, and scalability.

In recent years, Deep Learning (DL), such as the algorithms of Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and Generative Adversarial Networks (GAN), has been widely studied and applied in various fields including agriculture. Researchers in the fields of agriculture often use software frameworks without sufficiently examining the ideas and mechanisms of a technique. This article provides a concise summary of major DL algorithms, including concepts, limitations, implementation, training processes, and example codes, to help researchers in agriculture to gain a holistic picture of major DL techniques quickly. Research on DL applications in agriculture is summarized and analyzed, and future opportunities are discussed in this paper, which is expected to help researchers in agriculture to better understand DL algorithms and learn major DL techniques quickly, and further to facilitate data analysis, enhance related research in agriculture, and thus promote DL applications effectively. Keywords: deep learning, smart agriculture, neural network, convolutional neural networks, recurrent neural networks, generative adversarial networks, artificial intelligence, image processing, pattern recognition DOI: 10.25165/j.ijabe.20181104.4475 Citation: Zhu N Y, Liu X, Liu Z Q, Hu K, Wang Y K, Tan J L, et al. Deep learning for smart agriculture: Concepts, tools, applications, and opportunities. Int J Agric & Biol Eng, 2018; 11(4): 32-44.

Intelligent video surveillance systems with anomaly detection capabilities are indispensable for outdoor security. Video anomaly detection (VAD) is usually performed by learning patterns representing normal events and declaring an anomaly when an abnormal pattern is encountered. However, the features of normal patterns in a video often vary with time as real-world videos are non-stationary in nature, which makes its handling essential during VAD. To this end, we propose an approach for anomaly detection in videos, where a novel deep temporally recursive differencing network (DDN) diminishes the adverse effects of the non-stationary nature on VAD. The DDN consists of multiple layers of differencing operators of optimized orders, where every two consecutive layers are separated by a suitable nonlinearity. Spatial and temporal features are extracted from nonoverlapping blocks in video frames and fed to the DDN. While the spatial feature is obtained using a pretrained network, our temporal feature computation involves the use of FlowNetS with a new training strategy that does not require ground truth. The features at the output of DDN are used in a predictor based on autoregression and moving average of the regression errors. Then, the predictor's output estimates are compared to the corresponding actual values for anomaly detection, which also involves block-level selection and consistency check. Qualitative evaluation and quantitative comparison with several existing approaches on multiple standard datasets demonstrate the effectiveness of the proposed VAD approach. An ablation study highlighting the significance of the various components of our approach and a hyperparameter analysis are also provided.

Information Theory (IT) has been used in Machine Learning (ML) from early days of this field. In the last decade, advances in Deep Neural Networks (DNNs) have led to surprising improvements in many applications of ML. The result has been a paradigm shift in the community toward revisiting previous ideas and applications in this new framework. Ideas from IT are no exception. One of the ideas which is being revisited by many researchers in this new era, is Information Bottleneck (IB); a formulation of information extraction based on IT. The IB is promising in both analyzing and improving DNNs. The goal of this survey is to review the IB concept and demonstrate its applications in deep learning. The information theoretic nature of IB, makes it also a good candidate in showing the more general concept of how IT can be used in ML. Two important concepts are highlighted in this narrative on the subject, i) the concise and universal view that IT provides on seemingly unrelated methods of ML, demonstrated by explaining how IB relates to minimal sufficient statistics, stochastic gradient descent, and variational auto-encoders, and ii) the common technical mistakes and problems caused by applying ideas from IT, which is discussed by a careful study of some recent methods suffering from them.

Video anomaly detection (VAD) remains a challenging task in the pattern recognition community due to the ambiguity and diversity of abnormal events. Existing deep learning-based VAD methods usually leverage proxy tasks to learn the normal patterns and discriminate the instances that deviate from such patterns as abnormal. However, most of them do not take full advantage of spatial-temporal correlations among video frames, which is critical for understanding normal patterns. In this paper, we address unsupervised VAD by learning the evolution regularity of appearance and motion in the long and short-term and exploit the spatial-temporal correlations among consecutive frames in normal videos more adequately. Specifically, we proposed to utilize the spatiotemporal long short-term memory (ST-LSTM) to extract and memorize spatial appearances and temporal variations in a unified memory cell. In addition, inspired by the generative adversarial network, we introduce a discriminator to perform adversarial learning with the ST-LSTM to enhance the learning capability. Experimental results on standard benchmarks demonstrate the effectiveness of spatial-temporal correlations for unsupervised VAD. Our method achieves competitive performance compared to the state-of-the-art methods with AUCs of 96.7%, 87.8%, and 73.1% on the UCSD Ped2, CUHK Avenue, and ShanghaiTech, respectively.

Anomaly detection in surveillance videos is a challenging task due to the diversity of anomalous video content and duration. In this paper, we consider video anomaly detection as a regression problem with respect to anomaly scores of video clips under weak supervision. Hence, we propose an anomaly detection framework, called Anomaly Regression Net (ARNet), which only requires video-level labels in training stage. Further, to learn discriminative features for anomaly detection, we design a dynamic multiple-instance learning loss and a center loss for the proposed AR-Net. The former is used to enlarge the inter-class distance between anomalous and normal instances, while the latter is proposed to reduce the intra-class distance of normal instances. Comprehensive experiments are performed on a challenging benchmark: ShanghaiTech. Our method yields a new state-of-the-art result for video anomaly detection on ShanghaiTech dataset.

Deep Learning Applications in Primary Education: A Systematic Literature Review of Emerging Trends, Challenges, and Opportunities. Objectives: Despite significant advances in healthcare and finance, Deep Learning (DL) remains underutilized in primary education, with only about 12% of AI-related studies focusing on K–6. Given the formative role of early schooling in cognitive and social development, this systematic review analyzed recent empirical studies to identify key trends, challenges, and opportunities in applying DL to primary education. Methods: Following the PRISMA 2020 guidelines, 21 studies published between 2021 and 2025 across 11 countries were analyzed using a structured coding sheet and further examined with bibliometric mapping, Microsoft Excel, and Python-based visualization tools. The review included diverse sources focusing on DL use cases across global primary education systems. Findings: The findings reveal a global increase in DL adoption, led by China and South Korea, with growing contributions from Indonesia, India, and the Philippines. Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and transformer-based models are among the most commonly applied architectures to tasks such as personalized learning, early detection of learning difficulties, assessment automation, and curriculum development. However, there are still problems in the context of ethical concerns, including data privacy, algorithmic bias, and equity of access. Technical barriers involve dataset complexity, model generalization, and resource limitations, while pedagogical issues center on aligning DL applications with developmental needs and classroom realities. Despite these obstacles, DL demonstrates significant potential to enhance personalization, foster engagement, and support holistic educational outcomes. Conclusion: This review contributes a strategic roadmap for integrating DL in primary education by balancing innovation with pedagogy and ethics. Future research should prioritize cross-disciplinary collaboration, greater geographic diversity, and improvements in scalability and interpretability to ensure DL supports equitable, future-ready learning. Keywords: deep learning, primary education, personalized learning, systematic literature review, educational technology.

In this paper, we propose an improved hybrid approach for text generation that combines the strengths of generative adversarial networks (GANs) and recurrent neural networks (RNNs). The proposed model, named TextGen-GAN, uses a GAN to generate high-level semantic representations of text, which are then passed through an RNN decoder to produce coherent and diverse text. The suggested method produces superior results in terms of both quality and diversity when tested on a number of benchmark datasets. The obtained results show that the combination of GAN and RNN outperforms GAN alone, RNN only, baseline, and state-of-the-art models, demonstrating the effectiveness of the proposed hybrid approach.

Anomaly detection in crowd surveillance videos is a critical task for ensuring public safety and security. In this research paper, we propose a comprehensive framework for anomaly detection using deep learning techniques, specifically Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), and Generative Adversarial Networks (GANs). Our objective is to narrow the gap between computational complexity and detection effectiveness while satisfying the demand for explainability in anomaly detection systems. The proposed framework leverages the UCSD Ped2 dataset and focuses on incorporating spatial constraints to enhance anomaly detection performance. We introduce novel approaches for feature extraction and anomaly detection using GANs, aiming to capture essential spatial and temporal information while reducing computational complexity. Specifically, we develop encoders within the GAN framework to map input data to a lower-dimensional latent space and perform anomaly detection based on the similarity between latent codes. Through extensive experimentation and evaluation, we compare the performance of RNNs, CNNs, and GANs in detecting anomalies in crowd surveillance videos. Various evaluation metrics, including precision, recall, F1 score, ROC-AUC, and PR-AUC, is considered to assess the effectiveness of each approach. Additionally, they studied hyperparameters such as learning rate, batch size, network architecture, and GAN-specific parameters to optimize anomaly detection performance. Our results demonstrate the usefulness of the proposed framework in achieving robust and explainable anomaly detection in crowd surveillance videos. By combining advanced deep learning techniques with spatial constraints and explainability considerations, our research contributes to the development of more reliable and interpretable anomaly detection systems for real-world applications.

Transformer is a kind of deep neural network that relies on the technique of self-attention and used initially in the field of natural language processing. Scientists use transformer for computer vision (CV) applications because of its good data representation capabilities. Transformer-based models yield similar performance or surpass other network architectures, including convolutional and recurrent neural networks, in a variety of visual benchmarks. In this work, we investigate the methods for video anomaly detection (VAD) using vision transformer models in the recent literature. The main topics we explore comprise vision transformers used in CV applications with a special focus on VAD methods leveraging transformer architecture. We also briefly present anomaly detection methods based on transformers. Additionally, we address the advantages, challenges and current limitations of the transformer architecture as well as potential solutions to address the technical challenges. In the concluding section of this study, we offer avenues for further investigation concerning the use of vision transformers in VAD tasks.

Adopting an accurate anomaly detection mechanism is crucial for industrial software systems in order to prevent system outages that can deteriorate system availability. However, employing a supervised machine learning technique to detect anomalies in large production scale industrial software systems is highly impractical due to the requirement of annotated data. This raises the need for comprehensive semi-supervised and unsupervised anomaly detection mechanisms. This paper presents the application of Generative Adversarial Network (GAN) based models to detect system anomalies using semi-supervised one-class learning. We show that the use of a variant of GAN known as bidirectional GAN (BiGAN) gives augmented results when compared to the traditional GAN based anomaly detection, for the selected industrial system. Moreover, the experiments clearly show that the performance of the BiGAN has a direct correlation with the dimensions of the dataset used for training. The BiGAN even tends to outperform the well-established semi-supervised One-class SVM classifier and a prominent generative network for semi-supervised anomaly detection, Variational Autoencoders (VAEs) when the size of the feature space increases.

<p class="0abstract">The research aimed to conduct an extensive study of machine learning and deep learning methods in cybersecurity. To accomplish the objectives, the research carried out a qualitative study based on secondary data collection to review the available studies and literature. The research has examined three machine learning methods and three deep learning methods to study the most popular techniques used in cybersecurity. During the research, the working mechanism of each method was studied along with their strengths and weaknesses. Also, a comparative discussion has been made to examine the most effective method for cybersecurity. Limitations in the current literature were also identified, and future direction is also given to target and develop the weak areas of machine learning and deep learning methods.<em> </em><strong></strong></p>