235 Results Found

Notable progress was achieved by recent technologies. As the main goal of technology is to make daily life easier, we will investigate the development of an intelligent system for the assistance of impaired people in their navigation. For visually im...

Nowadays, the field of video-based action recognition is rapidly developing. Although Vision Transformers (ViT) have made great progress in static image processing, they are not yet fully optimized for dynamic video applications. Convolutional Neural...

With the development of smart agriculture, deep learning is playing an increasingly important role in crop disease recognition. The existing crop disease recognition models are mainly based on convolutional neural networks (CNN). Although traditional...

In recent years, segmentation for medical applications using Magnetic Resonance Imaging (MRI) has received increasing attention. Working in this field has emerged as an ambitious task and a major challenge for researchers; particularly, brain tumor s...

Fire detection remains a challenging task due to varying fire scales, occlusions, and complex environmental conditions. This paper proposes the CN2VF-Net model, a novel hybrid architecture that combines vision Transformers (ViTs) and convolutional ne...

Background/Objectives: Vision Transformers (ViTs) and convolutional neural networks (CNNs) have demonstrated remarkable performances in image classification, especially in the domain of medical imaging analysis. However, ViTs struggle to capture high...

Recently, Vision Transformers (ViTs) have been actively applied to fine-grained visual recognition (FGVR). ViT can effectively model the interdependencies between patch-divided object regions through an inherent self-attention mechanism. In addition,...

Crowd counting is a critical computer vision task with significant applications in public security and smart city systems. While deep learning has markedly improved accuracy, persistent challenges include extreme scale variations, severe occlusion, a...

Colorectal cancer (CRC) is the second most common global malignancy with high mortality, and timely early polyp detection is critical to halt its progression. Yet, polyp image segmentation—an essential tool—faces challenges: blurred edges...

The normal operation of rolling bearings is crucial to the performance and reliability of rotating machinery. However, the collected vibration signals are often mixed with complex noise, and the transformer network cannot fully extract the characteri...

Salient Object Detection (SOD) is gradually applied in natural scene images. However, due to the apparent differences between optical remote sensing images and natural scene images, directly applying the SOD of natural scene images to optical remote...

Image dehazing is a critical task in image restoration, aiming to retrieve clear images from hazy scenes. This process is vital for various applications, including machine recognition, security monitoring, and aerial photography. Current dehazing alg...

Drone-based object detection faces critical challenges, including tiny objects, complex urban backgrounds, dramatic scale variations, and high-frequency detail loss during feature propagation. Current detection methods struggle to address these chall...

In the production process of metal industrial products, the deficiencies and limitations of existing technologies and working conditions can have adverse effects on the quality of the final products, making surface defect detection particularly cruci...

Oriented object detection in aerial imagery presents unique challenges due to the arbitrary orientations, diverse scales, and limited availability of labeled data. In response to these issues, we propose RASST—a lightweight Rotationally Aware S...

Transmission line fittings have been exposed to complex environments for a long time. Due to the interference of haze and other environmental factors, it is often difficult for the camera to obtain high quality on-site images, and the traditional ima...

Vision–language pre-training (VLP) faces challenges in aligning hierarchical textual semantics (words/phrases/sentences) with multi-scale visual features (objects/relations/global context). We propose a hierarchical VLP model (HieVLP) that addr...

Automated segmentation algorithms for dermoscopic images serve as effective tools that assist dermatologists in clinical diagnosis. While existing deep learning-based skin lesion segmentation algorithms have achieved certain success, challenges remai...

To facilitate the realization of automated tea picking and enhance the speed and accuracy of tea leaf grading detection, this study proposes an improved YOLOv8 network for fresh tea leaf grading recognition. This approach integrates a Hierarchical Vi...

The reliable and efficient large-scale mapping of date palm trees from remotely sensed data is crucial for developing palm tree inventories, continuous monitoring, vulnerability assessments, environmental control, and long-term management. Given the...

Effectively capturing multi-scale object features is crucial for vision sensors used in road object detection tasks. Traditional spatial pyramid pooling methods fuse multi-scale feature information but lack adaptability in dynamically adjusting convo...

Skin cancer is a significant global health concern, with melanoma being the most dangerous form, responsible for the majority of skin cancer-related deaths. Early detection of skin cancer is critical, as it can drastically improve survival rates. Whi...

Soybean rust, caused by the fungus Phakopsora pachyrhizi, is recognized as the most devastating disease affecting soybean crops worldwide. In practical applications, performing accurate Phakopsora pachyrhizi segmentation (PPS) is essential for elucid...

Convolutional neural networks (CNNs) have made significant progress in the field of facial expression recognition (FER). However, due to challenges such as occlusion, lighting variations, and changes in head pose, facial expression recognition in rea...

Wild mushrooms are popular for their taste and nutritional value; however, non-experts often struggle to distinguish between toxic and non-toxic species when foraging in the wild, potentially leading to poisoning incidents. To address this issue, thi...

Forest fires have continually endangered personal safety and social property. To reduce the occurrences of forest fires, it is essential to detect forest fire smoke accurately and quickly. Traditional forest fire smoke detection based on convolutiona...

Deep learning has promoted the research of object detection in aerial scenes. However, most of the existing networks are limited by the large-scale variation of objects and the confusion of category features. To overcome these limitations, this paper...

Table detection in document images is a challenging problem due to diverse layouts, irregular structures, and embedded graphical elements. In this study, we present HTTD (Hierarchical Transformer for Table Detection), a cutting-edge model that combin...

An unescapable trend of hyperspectral image (HSI) has been toward classification with high accuracy and splendid performance. In recent years, Transformers have made remarkable progress in the HSI classification task. However, Transformer-based metho...

The central goal of few-shot scene classification is to learn a model that can generalize well to a novel scene category (UNSEEN) from only one or a few labeled examples. Recent works in the Remote Sensing (RS) community tackle this challenge by deve...

Rain is a typical meteorological event that affects the visual appeal of outdoor pictures. The presence of rain streaks severely blurs image details, negatively impacting subsequent computer visual tasks. Due to the challenge of acquiring authentic p...

Power transmission line icing (PTLI) poses significant threats to the reliability and safety of electrical power systems, particularly in cold regions. Accumulation of ice on power lines can lead to severe consequences, such as line breaks, tower col...

Emerging vision transformers (ViTs) are more powerful in modeling long-range dependences of features than conventional deep convolution neural networks (CNNs). Thus, they outperform CNNs in several computer vision tasks. However, existing ViTs fail t...

Transformers have recently gained significant attention in low-level vision tasks, particularly for remote sensing image super-resolution (RSISR). The vanilla vision transformer aims to establish long-range dependencies between image patches. However...

Vision Transformers (ViTs) have shown their superiority in various visual tasks for the capability of self-attention mechanisms to model long-range dependencies. Some recent works try to reduce the high cost of vision transformers by limiting the sel...

Few-shot semantic segmentation has attracted much attention because it requires only a few labeled samples to achieve good segmentation performance. However, existing methods still suffer from insufficient contextual information and unsatisfactory ed...

Pose estimation plays a crucial role in recognizing and analyzing the postures, actions, and movements of humans and animals using computer vision and machine learning techniques. However, bird pose estimation encounters specific challenges, includin...

In remote sensing single-image dehazing (RSSID), adjacency effects and the multi-scale characteristics of the land surface–atmosphere system highlight the importance of a network’s effective receptive field (ERF) and its ability to captur...

In recent years, the utilization of artificial intelligence methodologies in computer vision has markedly propelled the advancement of intelligent healthcare. A multimodal medical image segmentation algorithm is proposed by combining patient metadata...

Single-image dehazing is a pivotal challenge in computer vision that seeks to remove haze from images and restore clean background details. Recognizing the limitations of traditional physical model-based methods and the inefficiencies of current atte...

Transformers have been extensively utilized as encoders in medical image segmentation; however, the information that an encoder can capture is inherently limited. In this study, we propose MINTFormer, which introduces a Heterogeneous encoder that int...

Background: Breast cancer remains one of the leading causes of mortality among women worldwide, highlighting the critical need for accurate and efficient diagnostic methods. Methods: Traditional deep learning models often struggle with feature redund...

The Transformer model can capture global contextual information but does not have an inherent inductive bias. In contrast, convolutional neural networks (CNNs) are highly praised in computer vision due to their strong inductive bias and local spatial...

Image translation is a classic problem of image processing and computer vision for transforming an image from one domain to another by learning the mapping between an input image and an output image. A novel Multi-scale Residual Generative Adversaria...

Remote image semantic segmentation technology is one of the core research elements in the field of computer vision and has a wide range of applications in production life. Most remote image semantic segmentation methods are based on CNN. Recently, Tr...

The distinctive feature of hyperspectral images (HSIs) is their large number of spectral bands, which allows us to identify categories of ground objects by capturing discrepancies in spectral information. Convolutional neural networks (CNN) with atte...

Semantic segmentation is a fundamental task in remote sensing image analysis that aims to classify each pixel in an image into different land use and land cover (LULC) segmentation tasks. In this paper, we propose MeViT (Medium-Resolution Vision Tran...

Crowd counting is a significant task in computer vision. By combining the rich texture information from RGB images with the insensitivity to illumination changes offered by thermal imaging, the applicability of models in real-world complex scenarios...

Remote sensing (RS) images play an indispensable role in many key fields such as environmental monitoring, precision agriculture, and urban resource management. Traditional deep convolutional neural networks have the problem of limited receptive fiel...

The deep learning method for natural-image object detection tasks has made tremendous progress in recent decades. However, due to multiscale targets, complex backgrounds, and high-scale small targets, methods from the field of natural images frequent...