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Gish: a novel activation function for image classification
In Convolutional Neural Networks (CNNs), the selection and use of appropriate activation functions is of critical importance. It has been seen that...
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A class-aware multi-stage UDA framework for prostate zonal segmentation
Unsupervised domain adaptation (UDA) aims to solve the lack of annotation in a new dataset which has non-independent identity distribution compare...
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A comparative analysis of various activation functions and optimizers in a convolutional neural network for hyperspectral image classification
Hyperspectral imaging has a strong capability respecting distinguishing surface objects due to the ability of collect hundreds of bands along the...
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Content-based image retrieval through fusion of deep features extracted from segmented neutrosophic using depth map
The main challenge of content-based image retrieval systems is the difference between how images are described using algorithms and how humans...
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Improving interpretability via regularization of neural activation sensitivity
State-of-the-art deep neural networks (DNNs) are highly effective at tackling many real-world tasks. However, their widespread adoption in...
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Generating the Base Map of Regions Using an Efficient Object Segmentation Technique in Satellite Images
Satellite images find various applications today, of which segmenting the objects in a satellite image for map generation is widely used in disaster...
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Boosting Object Detection in Foggy Scenes via Dark Channel Map and Union Training Strategy
Most existing object detection methods in real-world hazy scenarios fail to handle the heterogeneous haze and treat clear images and hazy images as...
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Traffic Sign Image Segmentation Algorithm Based on Improved Spatio-Temporal Map Convolution
A traffic sign image segmentation algorithm based on improved spatio-temporal graph convolution is proposed by fusing octave convolution and...
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Classification of imbalanced multi-label leaf diseases using CaRiT: class attention enabled RegionViT
Plant diseases, particularly leaf diseases, are one of the factors contributing to crop loss, accounting for approximately 15-40% of the total loss...
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Stereo Visual Mesh for Generating Sparse Semantic Maps at High Frame Rates
The Visual Mesh is an input transform for deep learning that allows depth independent object detection at very high frame rates. The present study...
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Image super-resolution reconstruction based on deep dictionary learning and A+
The method of image super-resolution reconstruction through the dictionary usually only uses a single-layer dictionary, which not only cannot extract...
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Feature Extraction by Convolutional Neural Network
An optimal map** from pixel values to class labels by a MLP requires too many nodes and layers for successful training. Convolutional neural...
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Joint Bilateral-Resolution Identity Modeling for Cross-Resolution Person Re-Identification
Person images captured by public surveillance cameras often have low resolutions (LRs), along with uncontrolled pose variations, background clutter...
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MANet: An End-To-End Multiple Attention Network for Extracting Roads Around EHV Transmission Lines from High-Resolution Remote Sensing Images
Complete and accurate road network information is an important basis in the detection of EHV transmission lines, and regular updates of road...
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Comparative Study of Activation Functions and Their Impact on the YOLOv5 Object Detection Model
Object detection is an important aspect of computer vision research, involving determining the location and class of objects within a scene. For an...
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Localized Super Resolution for Foreground Images Using U-Net and MR-CNN
Images play a vital role in understanding data through visual representation. It gives a clear representation of the object in context. But if this...
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Attention Based Convolutional Neural Network with Multi-frequency Resolution Feature for Environment Sound Classification
The environmental sound classification has great research significance in the fields of intelligent audio monitoring and other fields. A novel...
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Soft-edge-guided significant coordinate attention network for scene text image super-resolution
Scene text image super-resolution (STISR) aims to enhance the resolution and visual quality of low-resolution scene text images, thereby improving...
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Lightweight global-locally connected distillation network for single image super-resolution
As convolutional neural networks (CNNs) have been commonly applied to ill-posed single image super-resolution (SISR) task, most previous CNN-based...
