In driver attention zone prediction tasks, accurately identifying and locating the driver’s attention zone is crucial. Traditional models have significant limitations in complex driving scenarios due to their failure to fully utilize multidimen...
Landslide susceptibility assessment (LSA) is crucial for regional landslide risk evaluation and mitigation strategy formulation. Previous studies mostly adopted single-scale features, while landslide formation is influenced by multi-scale factors, ma...
Convolutional Neural Networks (CNNs) have been widely used in video super-resolution (VSR). Most existing VSR methods focus on how to utilize the information of multiple frames, while neglecting the feature correlations of the intermediate features,...
The traditional Siamese object tracking algorithm uses a convolutional neural network as the backbone and has achieved good results in improving tracking precision. However, due to the lack of global information and the use of spatial and scale infor...
Image denoising requires simultaneous suppression of stochastic corruption and preservation of local texture and large-scale structure. This study presents the Multi-scale Attention Denoising Network (MADNet), an encoder–decoder residual archit...
In this study, we proposed a YOLOv8-based Multi-Level Multi-Head Attention mechanism utilizing EO and IR cameras to enable rapid and accurate detection of vessels of various sizes in maritime environments. The proposed method integrates the Scale-Sen...
Automatic music transcription (AMT) aims to convert raw audio signals into symbolic music. This is a highly challenging task in the fields of signal processing and artificial intelligence, and it holds significant application value in music informati...
The obstruction of vehicles by surrounding vehicles, obstacles, etc. is a common phenomenon in the practical application of automatic driving. In view of the problem that the vehicle’s vision is affected by the occlusion, the vehicle feature in...
Pavement crack detection is crucial for ensuring road safety and reducing maintenance costs. Existing methods typically use convolutional neural networks (CNNs) to extract multi-level features from pavement images and employ attention mechanisms to e...
The use of convolutional neural networks (CNN) for crowd counting has made significant progress in recent years; however, effectively addressing the scale variation and complex backgrounds remain challenging tasks. To address these challenges, we pro...
In recent years, deep learning methods have achieved remarkable success in hyperspectral image classification (HSIC), and the utilization of convolutional neural networks (CNNs) has proven to be highly effective. However, there are still several crit...
Underwater image analysis is affected by light scattering, wavelength-dependent attenuation, low contrast, and suspended particles, which reduce the discriminative visual features. Current multi-scale Vision Transformers are not well-suited to these...
Computer vision systems are insensitive to the scale of objects in natural scenes, so it is important to study the multi-scale representation of features. Res2Net implements hierarchical multi-scale convolution in residual blocks, but its random grou...
Three-dimensional object detection in the point cloud can provide more accurate object data for autonomous driving. In this paper, we propose a method named MA-MFFC that uses an attention mechanism and a multi-scale feature fusion network with ConvNe...
The multi-scale lightweight network and attention mechanism recently attracted attention in person re-identification (ReID) as it is capable of improving the model’s ability to process information with low computational cost. However, state-of-the-ar...
In recent years, deep convolutional neural networks with multi-scale features have been widely used in image super-resolution reconstruction (ISR), and the quality of the generated images has been significantly improved compared with traditional meth...
Recently, with the development of convolutional neural networks, single-image super-resolution (SISR) has achieved better performance. However, the practical application of image super-resolution is limited by a large number of parameters and calcula...
Research studies have established that mindfulness helps in psychological well-being, stress reduction, chronic pain management, behavioral therapy, and other areas including organizational development. Mindfulness often refers to a state of consciou...
Object counting is a fundamental task in remote sensing analysis. Nevertheless, it has been barely studied compared with object counting in natural images due to the challenging factors, e.g., background clutter and scale variation. This paper propos...
Accurate segmentation of retinal blood vessels is a key step in the diagnosis of fundus diseases, among which cataracts, glaucoma, and diabetic retinopathy (DR) are the main diseases that cause blindness. Most segmentation methods based on deep convo...
Birdsong is a valuable indicator of rich biodiversity and ecological significance. Although feature extraction has demonstrated satisfactory performance in classification, single-scale feature extraction methods may not fully capture the complexity o...
Current single-view image inpainting methods often suffer from low image information utilization and suboptimal repair outcomes. To address these challenges, this paper introduces a novel image inpainting framework that leverages a structure-guided m...
This research presents an advanced YOLOv8-MMD framework specifically designed for intelligent white radish harvesting systems, addressing the critical need for simultaneous species recognition and quality evaluation. The proposed architecture is buil...
Accurate medical image segmentation can assist doctors in observing lesion areas and making precise judgments. Effectively utilizing important multi-scale semantic information in local and global contexts is key to improving segmentation accuracy. In...
Image super resolution (SR) is an important image processing technique in computer vision to improve the resolution of images and videos. In recent years, deep convolutional neural network (CNN) has made significant progress in the field of image SR;...
Due to the inherent limitations of a single viewpoint, reconstructing 3D human meshes from a single image has long been a challenging task. While deep learning networks enable us to approximate the shape of unseen sides, capturing the texture details...
Thangka images contain complex and interconnected elements that often share similar hues. Traditional segmentation algorithms struggle to capture fine details, which renders them poorly adaptable to this characteristic of Thangka images. Additionally...
In recent years, the application of multi-scale residuals and attention mechanisms in automatic speech recognition has made some progress. However, existing speech recognition systems still have critical limitations. Many existing systems fail to ade...
Diabetic retinopathy (DR) is a major cause of preventable vision loss, and its early detection is essential for timely clinical intervention. However, existing deep learning-based DR recognition methods still face two fundamental challenges: substant...
Gesture recognition is an important direction in computer vision research. Information from the hands is crucial in this task. However, current methods consistently achieve attention on hand regions based on estimated keypoints, which will significan...
We propose GourmetNet, a single-pass, end-to-end trainable network for food segmentation that achieves state-of-the-art performance. Food segmentation is an important problem as the first step for nutrition monitoring, food volume and calorie estimat...
The segmentation of retinal vessels is critical for the diagnosis of some fundus diseases. Retinal vessel segmentation requires abundant spatial information and receptive fields with different sizes while existing methods usually sacrifice spatial re...
Enhancing low-light images is crucial in computer vision applications. Most existing learning-based models often struggle to balance light enhancement and color correction, while images typically contain different types of information at different le...
Dental diseases are among the most prevalent diseases globally, and accurate segmentation of dental calculus images plays a crucial role in periodontal disease diagnosis and treatment planning. However, the current methods are not stable and reliable...
Remote sensing image dehazing (RSID) aims to remove haze from remote sensing images to enhance their quality. Although existing deep learning-based dehazing methods have made significant progress, it is still difficult to completely remove the uneven...
Non-intrusive load disaggregation (NILD) is of great significance to the development of smart grids. Current energy disaggregation methods extract features from sequences, and this process easily leads to a loss of load features and difficulties in d...
The Mamba network has recently emerged as a powerful architecture for remote sensing change detection (RSCD). Notably, the fusion of multi-scale features and heterogeneous attention mechanisms has proven effective in enhancing the performance of Mamb...
Compared with traditional machine learning algorithms, the convolutional neural network (CNN) has an excellent automatic feature learning ability and can complete the nonlinear representation from original data input to output by itself. However, the...
Many MRI-based Alzheimer’s disease (AD) classification studies report near-perfect accuracy; however, these results are often inflated by data leakage caused by slice-level splitting, where correlated slices of the same subject appear in both t...
With the rapid development of computer vision and machine vision, methods based on deep learning have achieved good results in the field of object detection, identification, and tracking. However, for the detection and identification of rebars in sma...
Short-term load forecasting is an important prerequisite for smart grid controls. The current methods are mainly based on the convolution neural network (CNN) or long short-term memory (LSTM) model to realize load forecasting. For the multi-factor in...
Heatmap-based traditional approaches for estimating human pose usually suffer from drawbacks such as high network complexity or suboptimal accuracy. Focusing on the issue of multi-person pose estimation without heatmaps, this paper proposes an end-to...
To overcome the limitations of traditional methods in reverberant and noisy environments, a robust multi-scale fusion neural network with attention mask is designed to improve direction-of-arrival (DOA) estimation accuracy for acoustic sources. It co...
Synthetic aperture radar (SAR) ship detection faces three persistent challenges: coherent speckle noise that obscures target boundaries, heterogeneous background clutter in coastal and harbor scenes, and ship targets whose spatial extent varies by mo...
Due to the rapid development of deep learning and artificial intelligence techniques, denoising via neural networks has drawn great attention due to their flexibility and excellent performances. However, for most convolutional network denoising metho...
Monocular Depth Estimation (MDE) remains a challenging problem due to texture ambiguity, occlusion, and scale variation in real-world scenes. While recent deep learning methods have made significant progress, maintaining structural consistency and ro...
Blind image watermarking models such as HiDDeN have laid an important foundation for end-to-end watermarking. Nevertheless, they still suffer from three major limitations: single-scale feature extraction, fixed fusion weights, and slow training conve...
The accurate detection of unmanned aerial vehicles (UAVs) in various sizes played an important role in the practical applications. Yet the preceding works suffered from the missing inference, the false alarms, and the poor accuracy due to the the adv...
As autonomous driving systems shift from experimental testing environments to real-world open-road deployment, object detection in traffic scenes has emerged as a critical challenge in the perception pipeline. In complex traffic scenarios, significan...
Accurately segmenting building roofs from satellite images is crucial for evaluating the photovoltaic power generation potential of urban roofs and is a worthwhile research topic. In this study, we propose an attention-based full-scale fusion (AFSF)...
of 268