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Generative models have emerged as potential tools for creating high-quality images, videos, and text. This paper explores the application of generative models in automating vlog content creation. It addresses both static and dynamic visual elements, eliminating the need for human intervention. Traditional vlogs often require specific environmental conditions and proper lighting for the vlog creation. To streamline this process, an automated system utilizing the generative models is proposed here. Generative models excel at generating realistic content that seamlessly integrates with real-world content. They enhance overall video quality and introduce creative elements by generating new scenes and backgrounds. This paper categorizes various generative modeling techniques based on frame elements and foreground-background conditions. It offers a comparative analysis of different generative model variants tailored for specific objectives. Furthermore, the paper reviews existing research on generative models for video and image content generation, visual quality enhancement, diversity, and coherence outcomes. Additionally, the paper highlights practical uses of the generative model for content creation in various contexts, such as face swapping, scene translation, and virtual content insertion. The paper also examines the public datasets used to train generative models. These datasets contain diverse visual content such as celebrity images, urban landscapes, and everyday scenes. [ABSTRACT FROM AUTHOR]

The maturity of 5G technology provides a guarantee for increasingly large communication networks, while the resources required for communication and computation are also increasing, and reasonable resource allocation can improve the efficiency of network communication and reduce the consumption of communication resources. Existing deep learning methods have been able to predict network traffic to a certain extent, so as to solve the communication efficiency and resource consumption problems in the field of integrated sensing, communication and computation (ISCC) through rational resource allocation. However, the following problems still exist: (1) The feature learning ability of the prediction model is insufficient, and the prediction accuracy needs to be improved. (2) Powerful and complex deep learning methods lead to an increase in the prediction cost of the model. To address these problems, this paper proposes a deep learning method based on a lightweight hybrid attention network. In order to capture the key features of 5G data more effectively, an efficient hybrid attention mechanism (EHA) is proposed. After this attention is applied to convolution, the key information can be well enhanced. We use depthwise separable convolution in feature extraction, which greatly improves the efficiency of lightweight convolution layer (LC) in feature extraction. Combined with the efficient hybrid attention mechanism (EHA), the proposed model has better lightweight properties. Experimental results show that the model proposed in this paper has lower RMSE and MAE values on the three datasets, as well as fewer parameters and computational effort compared to the baseline scheme. [ABSTRACT FROM AUTHOR]

• The paper presents a lightweight model with satisfactory performance in complex industrial noise environments. • Data fusion using data from multiple sensors for more complete fault information. • Design a unique grayscale feature map based on IMF components to obtain multi-sensor and multi-frequency band fault features. • Design a UL-GoogLeNet lightweight fault diagnosis model based on GoogLeNet and ULSAM. As artificial intelligence and sensor technology develop rapidly, intelligent fault diagnosis methods based on deep learning are widely used in industrial production. However, in practical industrial applications, the complex noise environment affects the performance of the diagnostic model, and the huge model parameters cannot meet the requirements of low cost and high performance in industrial production. To address the above problems, this paper proposes a lightweight intelligent fault diagnosis model using multi-sensor data fusion that not only meets the lightweight requirements of "small, light, and fast", but also realizes high accuracy diagnosis in noisy environments. Firstly, the vibration signals from different sensors of rolling bearings are processed using the variational mode decomposition (VMD) to design a unique method of constructing grayscale feature maps based on each intrinsic modal function (IMF) component. Then, the ultra-lightweight GoogLeNet model (UL-GoogLeNet) is constructed to adjust the traditional GoogLeNet structure, while the Ultra-lightweight subspace attention module (ULSAM) is introduced to reduce the model parameters and enhance the feature extraction capability. UL-GoogLeNet is trained and tested by dividing the grayscale feature maps into training and testing sets to realize the intelligent recognition of different fault types in rolling bearings. Experiments are conducted on two datasets and compared with multiple methods, and the final experimental results prove the effectiveness and superiority of the proposed method in this paper. [ABSTRACT FROM AUTHOR]

Brain-computer interface (BCI) provides a platform for humans to communicate using Electroencephalogram (EEG) signals by converting them into commands that can be used by the output device to perform the desired tasks. This paper focuses on the identification of vowels from EEG signals. First, a dataset of EEG signals has been created for the identification of vowels by collecting data using a 14-channel EEG device Emotiv- epoc+ from 16 subjects. Then, a deep learning-based model is proposed using a multi-headed Convolutional Neural Network for feature extraction and classification of imagined speech of vowels. Butterworth lowpass and bandpass filter of order five are implemented for denoising and sub-banding of the EEG signals which are further pre-processed using Hilbert Huang Transform. The model has achieved an average accuracy of 97.67% with a five-fold cross-validation technique using all six sub-bands of the EEG signals. The model has achieved an average precision and recall of 95.54% and 95.11% respectively. The proposed model is statistically tested using the Mann-Whitney U test and paired t-test with a p-value less than 0.05. [ABSTRACT FROM AUTHOR]

Recently, deep learning methods have achieved excellent performance in single image super-resolution (SISR), but most existing methods suffer from heavy computational costs and memory storage. To address this problem, some lightweight SR methods have been proposed, among which convolutional neural network (CNN) with attention mechanisms has received increasing attention. However, the existing CNN-based lightweight network and attention mechanism still have redundancy in feature extraction and enhancement. This paper presents Efficient Self-calibrated and Hierarchical Refinement Network (ESHR) for Lightweight Super-Resolution that includes three effective designs. First, we propose a more effective feature extraction basic module called Self-calibrated Residual Block (SRB), which can achieve superior performance when replacing other common convolution modules such as traditional convolution, BSConv and DSConv. Second, we propose a more lightweight spatial attention mechanism called Squeeze-Excitation Spatial Attention (SESA), which can interact and enhance features more effectively in spatial dimensions to improve the representation ability of the model. Third, we design an Efficient Hierarchical Refinement Block (EHRB) to integrate and utilize multi-level features, which can achieve better performance than single-level refinement without increasing the number of parameters. Extensive experiments demonstrate that the proposed method outperforms state-of-the-art CNN-based methods quantitatively and qualitatively. [ABSTRACT FROM AUTHOR]

Anomaly detection is distinguishing unusual objects from normal patterns. It is a complex task due to unpredictable nature of anomalies, which can appear in many forms or they can be hidden by mimicking normal behaviors in a graph structure. Such diversity makes this Deep learning approaches can solve these problems by extracting complex patterns from networks. However, addressing different forms of anomalous instances is essential for successfully implementing these approaches, as different anomaly types require further analysis. Additionally, it is challenging to interpret anomalies beforehand without focusing on every aspect of anomalies. Our objective is to propose an architecture capable of handling all types of anomalous entities by tackling challenges across various domains. In this paper, we introduce ARNAD, a novel framework that integrates three deep models to identify anomalies in graphs: graph neural network, autoencoder, and adversarial autoencoder. ARNAD approaches graph anomaly detection by utilizing the features of the deep parts, and four key elements stand out: (1) the autoencoder learns the overall graph structure and identifies highly deviated ones, (2) the graph neural network exploits graph structure to detect anomalies among the communities, (3) a fixed-size randomized neighborhood that prevents overfitting while reducing complexity (4) the adversarial autoencoder improves the robustness of the framework and discriminates anomalies. To detect anomalies, four receptive components assign risk scores to objects in the attributed network. We evaluated the framework with three synthetic datasets that simulate different behaviors of anomalies and six widely used real attributed networks. Our experimental results show that ARNAD performs competitively with other state-of-the-art models in detecting anomalous entities while minimizing false positives, demonstrating ARNAD's effectiveness in detecting graph anomalies. [ABSTRACT FROM AUTHOR]

Most learned image compression methods focus on reducing the average code length of the image, which is unreasonable for the human visual system. Thus, appropriate bit allocation gains significance. This paper presents a novel end-to-end method for perceptual image compression, emphasizing content prioritization and a region-based hierarchical strategy. Firstly, our modified class activation mapping (CAM) can serve as the visual perception network for simulating human visual perception. On this basis, we develop an efficient compression system that leverages a discretized hybrid entropy model to allocate bits automatically according to the perceptual prioritization of image content. In our compression system, an image is compressed jointly and hierarchically with different standards, guided by the importance map extracted by the visual perception network. We introduce content-weighted perceptual metrics to evaluate visual quality more reasonably and objectively. Experiments on publicly available datasets demonstrate that our method outperforms traditional codecs and recent content-oriented learned methods in overall performance. Compared with other methods, the proposed method reconstructs images that are more friendly to the human visual system. [ABSTRACT FROM AUTHOR]

Currently, weapon detection through video surveillance has been extensively studied using deep learning techniques by researchers. However, limited attention has been given to the detection of weapons in night time or dark scenarios. This paper aims to address this gap by proposing a novel approach for weapon detection specifically modified for low-light conditions. The authors demonstrate accurate and robust detection of weapons in challenging nighttime environments by modifying the deep learning model YOLOv7. The YOLOv7-DarkVision model has been developed by combining a brightening algorithm with the advanced image processing techniques and architecture of YOLOv7. A dataset of 15,367 images were collected for training of the model, along with five dark videos from various sources for performance evaluation. The derived detection model, which has a precision score of 95.50% and an F1-Score of 93.41%, performs absolutely well as a weapon detector. [ABSTRACT FROM AUTHOR]

Electroencephalogram (EEG) signals contains a major role in examining the behavior of brain activity. Moreover, these signals are contaminated with artifacts, which may affect the EEG signal analysis. The EEG performs as the low spatial resolution in the unimodal analysis. Owing to the highly time-consumption procedure, the removal of artifact techniques cannot be applied in practical applications. In existing works, automatic artifact removal techniques are ineffective for eliminating the artifacts in the EEG signals. Thus, the core of this concept is to design a low complexity aware, intelligent artifacts removal model using deep learning. This paper attempts to introduce a 1-Dimensional-Convolutional Neural Network with a Weighted Deep Feature (1D-CNN-WDF), which acts as a filter to remove the unnecessary noise in EEG signals. The WDF extraction in 1D-CNN-WDF is developed by combining the Coyote Optimization Algorithm (COA), and Bird Swarm Algorithm (BSA) named Hybrid Bird-COA (HB-COA). The same hybrid algorithm is used for the tuning of model parameters in 1D-CNN-WDF. The analysis results confirm that the recommended method is more enriched than recently introduced methods when considering the improved Signal to Noise Ratio (SNR), less Mean Squared Error (MSE), and reduced computational complexity, which shows that the suggested method is attained the superior reconstruction quality. [ABSTRACT FROM AUTHOR]

Glaucoma is a prevalent eye disease that is responsible for causing blindness worldwide. To diagnose glaucoma, the cup-to-disc ratio (CDR) is an important factor. We introduce MRSNet, a novel segmentation network that incorporates encoding and decoding structures. The key innovation of this network is the application of convolutional block with large kernel convolutional attention to the task of medical image segmentation for optic cup and disc. By combining the benefits of residual and self-attention, our network achieves improved performance. The coding region of the network utilizes convolutional block with large kernel convolutional attention, enabling the extraction of multi-scale features with lower computational resources while also enhancing spatial attention. The self-attention layer acts as a transition between the encoding and decoding regions, capturing long connection information and providing additional image details. To further enhance segmentation performance, we employ a multi-resolution image combination approach and adaptively extract the input form using the compression and excitation module. Additionally, we propose a novel approach that combines the principle of consistency of deep supervision mechanisms with cross-entropy and Dice loss to guide the network towards accurate segmentation. In this study, we utilized a five-fold cross-validation method to train our network model. We then performed experimental validation and evaluation on three widely-used datasets, namely REFUGE, DRISHTI-GS, and RIM-ONE-r3. Our model achieved impressive results in the cup-to-disc ratio metric, which accurately reflects the segmentation effect. Specifically, we achieved scores of 0.0242, 0.0941, and 0.0158 for the aforementioned datasets, respectively. These scores outperformed some current classical algorithms. The experimental results demonstrate that the method proposed in this paper has the capability to extract more comprehensive information about the optic cup and disc, with the ability to generalize across different datasets. Furthermore, it shows that the convolutional block with large kernel convolutional attention module can be effectively utilized for the segmentation task of optic cup and disc. These findings provide a valuable research foundation for future researchers. [ABSTRACT FROM AUTHOR]

Near-field multiple-input multiple-output (MIMO) radar imaging systems have recently gained significant attention. These systems generally reconstruct the three-dimensional (3D) complex-valued reflectivity distribution of the scene using sparse measurements. Consequently, imaging quality highly relies on the image reconstruction approach. Existing analytical reconstruction approaches suffer from either high computational cost or low image quality. In this paper, we develop novel non-iterative deep learning-based reconstruction methods for real-time near-field MIMO imaging. The goal is to achieve high image quality with low computational cost at compressive settings. The developed approaches have two stages. In the first approach, physics-based initial stage performs adjoint operation to back-project the measurements to the image-space, and deep neural network (DNN)-based second stage converts the 3D backprojected measurements to a magnitude-only reflectivity image. Since scene reflectivities often have random phase, DNN processes directly the magnitude of the adjoint result. As DNN, 3D U-Net is used to jointly exploit range and cross-range correlations. To comparatively evaluate the significance of exploiting physics in a learning-based approach, two additional approaches that replace the physics-based first stage with fully connected layers are also developed as purely learning-based methods. The performance is also analyzed by changing the DNN architecture for the second stage to include complex-valued processing (instead of magnitude-only processing), 2D convolution kernels (instead of 3D), and ResNet architecture (instead of U-Net). Moreover, we develop a synthesizer to generate large-scale dataset for training the neural networks with 3D extended targets. We illustrate the performance through experimental data and extensive simulations. The results show the effectiveness of the developed physics-based learned reconstruction approach compared to commonly used approaches in terms of both runtime and image quality at highly compressive settings. Our source codes and dataset are made available at https://github.com/METU-SPACE-Lab/Efficient-Learned-3D-Near-Field-MIMO-Imaging upon publication to advance research in this field. • 3D image reconstruction methods are developed for near-field MIMO radar imaging using deep learning-based direct inversion and 3D CNNs. • A synthesizer to generate 3D scenes with extended targets is developed to obtain large data for training the neural networks. • Resolution analysis is performed for compressive imaging settings with sparse arrays. • Performance is comprehensively evaluated with simulated and experimental data, using both magnitude-only and complex-valued processing. • The results show the effectiveness of the developed physics-based learned reconstruction method in terms of image quality and runtime. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to audio-visual consistency judgment (AVCJ), in which vowel-like regions exhibiting significant changes in lip shape are used as key pronunciations. Unlike conventional methods that typically apply speech information to the whole utterance and corresponding visual features, the proposed technique involves key pronunciation analysis and frontal lip reconstruction for non-frontal lip frames corresponding to these key pronunciations. This is done to eliminate the influence of variations in video viewing angle on consistency judgment and address the limitations of whole-sentence analysis in a process consisting of four steps. First, key pronunciations are detected in speech utterance. Second, view angle classification is performed on lip frames corresponding to the key pronunciations and then frontal reconstruction is performed by a proposed SL-CycleGAN. Third, deep correlation analysis is applied to the covariance of the deep features obtained from both audio and visual features. Fourth, time-delay and correlation differences are combined to estimate the joint score of each key pronunciation. The results of AVCJ are then acquired by calculating a sentence level score from each joint score. Experimental results from the OuluVS2 bimodal multi-view database showed that the proposed method outperformed several state-of-the-art algorithms, including quadratic mutual information (QMI), space-time canonical correspondence analysis, and multiple variations of SyncNet. [ABSTRACT FROM AUTHOR]

Due to the variety of documents and people's different stamping habits, it is difficult for current models to achieve satisfactory results in the process of extracting and removing rubber stamps. Most current methods cannot extract or remove rubber stamps without destroying the background text information. To solve the above problems, we propose a neural network that uses a double-cycle structure, named stamp extraction and removal generative adversarial network (SERGAN). In detail, we improve SERGAN's cycle-consistency by simultaneously recombining the generated rubber stamp image and unstamped image into a stamped image, which enables SERGAN to have multi-domain image conversion capability. To prove the model's generalization ability, we compare it with current mainstream methods on the public watermark dataset CLWD, and the experimental results show the excellence of our model. In addition, there is no publicly available dataset for rubber stamp extraction and removal experiments. To address the scarcity of rubber stamp datasets, we construct a large-scale rubber stamp dataset named LRSD. • A network with strong cycle-consistency which can simultaneously extract and remove rubber stamps from documents is proposed. • A multi-domain image conversion structure which can reduce the number of parameters is proposed. • A large-scale rubber stamp dataset for stamp extraction and removal task is constructed. [ABSTRACT FROM AUTHOR]