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Poly-cam: high resolution class activation map for convolutional neural networks
The demand for explainable AI continues to rise alongside advancements in deep learning technology. Existing methods such as convolutional neural...
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UM-CAM: Uncertainty-weighted Multi-resolution Class Activation Maps for Weakly-supervised Fetal Brain Segmentation
Accurate segmentation of the fetal brain from Magnetic Resonance Image (MRI) is important for prenatal assessment of fetal development. Although deep...
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Exploring class-agnostic pixels for scribble-supervised high-resolution salient object detection
Successful salient object detection is largely dependent on large-scale fine-grained annotated datasets. However, pixel-level annotation is a...
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VolPAM: Volumetric Phenotype-Activation-Map for data-driven discovery of 3D imaging phenotypes and interpretability
Knowledge about the subtypes of a disease critically affects clinical decisions ranging from the choice of therapeutic options to patient management....
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Multi-action Prediction Using an Iterative Masking Approach with Class Activation Map**
While prediction techniques for multiple objects in images have become increasingly sophisticated, predicting multiple actions in videos remains...
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An optimization high-resolution network for human pose recognition based on attention mechanism
In the high-resolution network (HRNet), the low layer of low resolution part can adopt shallow parallel network structure to maintain the...
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Web pages from mockup design based on convolutional neural network and class activation map**
The objective of this study is to validate the use of Deep Neural Networks (DNNs) to segment and classify web elements. To achieve this, a dataset of...
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Few-shot segmentation based on high-resolution representation and Brownian distance covariance learning
The purpose of the few-shot segmentation task is to segment images containing new categories using only a few labeled samples. Existing methods...
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Density Map Augmentation-Based Point-to-Point Vehicle Counting and Localization in Remote Sensing Imagery with Limited Resolution
Monitoring traffic flow is of great significance to contemporary urban management and intelligent transportation construction. Among them, satellite...
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Enhanced multi-level features for very high resolution remote sensing scene classification
Very high resolution (VHR) remote sensing (RS) scene classification is a challenging task due to the higher inter-class similarity and intra-class...
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Using GANs for Domain Adaptive High Resolution Synthetic Document Generation
In this paper we are addressing one specific problem of Context-Adaptive Document Analysis: the generation of specific learning data. While many...
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Medical Image Super Resolution by Preserving Interpretable and Disentangled Features
State of the art image super resolution (ISR) methods use generative networks to produce high resolution (HR) images from their low resolution (LR)...
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Deep convolutional encoder–decoder networks based on ensemble learning for semantic segmentation of high-resolution aerial imagery
Due to the complexity of object information and optical conditions of high-resolution aerial imagery, it is difficult to obtain fine semantic...
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SuperVessel: Segmenting High-Resolution Vessel from Low-Resolution Retinal Image
Vascular segmentation extracts blood vessels from images and serves as the basis for diagnosing various diseases, like ophthalmic diseases....
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HighBoostNet: a deep light-weight image super-resolution network using high-boost residual blocks
Image distortion is an inevitable part of the image acquisition process, which negatively affects the high-frequency contents of the images....
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High-frequency channel attention and contrastive learning for image super-resolution
Over the last decade, convolutional neural networks (CNNs) have allowed remarkable advances in single image super-resolution (SISR). In general,...
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Efficient CNN for high-resolution remote sensing imagery understanding
The analysis of remote sensing data and the classification of images are complex problems because understanding spatial patterns and intricate...
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Extraction Method of Rotated Objects from High-Resolution Remote Sensing Images
In recent years, the rapid development of remote sensing technology has made intelligent interpretation possible. However, remote sensing images have...
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Salient feature fusion convolutional network for multi-class meters detection
Automatic meter reading via deep learning and computer vision have become feasible for ensuring safe and stable substation operation. Meter model...
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Scalable approach for high-resolution land cover: a case study in the Mediterranean Basin
The production of land cover maps is an everyday use of image classification applications on remote sensing. However, managing Earth observation...
