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FPFnet: Image steganalysis model based on adaptive residual extraction and feature pyramid fusion
Image steganalysis is a technique for detecting images that contain hidden information. Convolutional neural networks have shown great potential in...
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Hierarchical pose net: spatial hierarchical body tree driven multi-person pose estimation
In this paper, we explore multi-level semantic information of human body structure and propose a paradigm for bottom-up multi-person pose estimation....
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Micro-expression action unit recognition based on dynamic image and spatial pyramid
Most of the existing studies have focused on the expression recognition of micro-expressions, while little research has been done on how to recognize...
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SFPN: segmentation-based feature pyramid network for multi-focus image fusion
In multi-focus image fusion, different targets often have different sizes, and the network with poor multi-scale feature extraction ability will...
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Learning Discriminated Features Based on Feature Pyramid Networks and Attention for Multi-scale Object Detection
As the research scene in object detection becomes increasingly complex, the extracted feature information needs to be further improved. Many...
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An effective reconstructed pyramid crosspoint fusion for multimodal infrared and visible images
Fusion for multimodal infrared and visible images refers to the process of combining detail from both infrared and visible images, which holds...
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MPF6D: masked pyramid fusion 6D pose estimation
Object pose estimation has multiple important applications, such as robotic gras** and augmented reality. We present a new method to estimate the...
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Vehicle object counting network based on feature pyramid split attention mechanism
In recent years, real-time vehicle congestion detection has become a hot research topic in the field of transportation due to the frequent occurrence...
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Context-aware pyramid attention network for crowd counting
Achieving accurate crowd counting still faces many challenges due to continuous scale variations. To this end, we present an innovative Context-Aware...
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Pyramid-dilated deep convolutional neural network for crowd counting
Statistics on crowds in crowded scenes can reflect the density level of crowds and provide safety warnings. This is a laborious task if conducted...
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DLMP-Net: A Dynamic Yet Lightweight Multi-pyramid Network for Crowd Density Estimation
The current deep neural networks used for crowd density estimation face two main problems. First, due to different surveillance distance from the...
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Finger vein recognition: utilization of adaptive gabor filters in the enhancement stage combined with SIFT/SURF-based feature extraction
Inadequacies of traditional means of human recognition along with one’s possession of unique physiological traits have paved the way for a more...
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Ψnet: a parallel network with deeply coupled spatial and squeezed features for segmentation of medical images
The process of delineating a region of interest or an object in an image is called image segmentation. Efficient medical image segmentation can...
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Cognitively-Inspired Multi-Scale Spectral-Spatial Transformer for Hyperspectral Image Super-Resolution
The hyperspectral image (HSI) super-resolution (SR) without auxiliary high-resolution images is a challenging task in computer vision applications....
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Automatic Brain Tumor Segmentation with a Bridge-Unet Deeply Supervised Enhanced with Downsampling Pooling Combination, Atrous Spatial Pyramid Pooling, Squeeze-and-Excitation and EvoNorm
Segmentation of brain tumors is a critical task for patient disease management. Since this task is time-consuming and subject to inter-expert...
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RSA-fusion: radar spatial attention fusion for object detection and classification
Object detection and classification in urban transportation scenarios lack robustness on a single sensor under extreme weather conditions....
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Domain-free fire detection using the spatial–temporal attention transform of the YOLO backbone
Conventional fire detection approaches typically relied on distinct models to address the varying characteristics of fires and smoke, particularly...
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SKGDC: Effective Segmentation Based Deep Learning Methodology for Banana Leaf, Fruit, and Stem Disease Prediction
In agriculture, detecting plant diseases is crucial for optimal plant growth. Initially, input images are collected from three datasets: banana leaf...
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Dense-scale dynamic network with filter-varying atrous convolution for semantic segmentation
Deep convolution neural networks (DCNNs) in deep learning have been widely used in semantic segmentation. However, the filters of most regular...
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Toward enhancing concrete crack segmentation accuracy under complex scenarios: a novel modified U-Net network
Convolutional neural networks (CNNs) have demonstrated promising accuracy in segmenting concrete cracks under controlled conditions. However, these...
