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Attention attention everywhere: Monocular depth prediction with skip attention
Abstract Monocular Depth Estimation (MDE) aims to predict pixel-wise depth given a single
RGB image. For both, the convolutional as well as the recent attention-based models …
RGB image. For both, the convolutional as well as the recent attention-based models …
ST-unet: Swin transformer boosted U-net with cross-layer feature enhancement for medical image segmentation
Medical image segmentation is an essential task in clinical diagnosis and case analysis.
Most of the existing methods are based on U-shaped convolutional neural networks (CNNs) …
Most of the existing methods are based on U-shaped convolutional neural networks (CNNs) …
Rs-net: Residual Sharp U-Net architecture for pavement crack segmentation and severity assessment
U-net, a fully convolutional network-based image segmentation method, has demonstrated
widespread adaptability in the crack segmentation task. The combination of the semantically …
widespread adaptability in the crack segmentation task. The combination of the semantically …
Augmenting DenseNet: Leveraging Multi-Scale Skip Connections for Effective Early-Layer Information Incorporation
The proliferation of Convolutional Neural Network-based applications in recent years has
necessitated the development of effective strategies for information and gradient flow within …
necessitated the development of effective strategies for information and gradient flow within …
Fusing Multispectral and LiDAR Data for CNN-Based Semantic Segmentation in Semi-Arid Mediterranean Environments: Land Cover Classification and Analysis
Spectral confusion among land cover classes is quite common, let alone in a complex and
heterogenous system like the semi-arid Mediterranean environment; thus, employing new …
heterogenous system like the semi-arid Mediterranean environment; thus, employing new …
DwinFormer: Dual window transformers for end-to-end monocular depth estimation
Depth estimation using monocular vision sensors is crucial in computer vision, with diverse
applications ranging from autonomous driving to robot motion. Conventional methods suffer …
applications ranging from autonomous driving to robot motion. Conventional methods suffer …
Leveraging U-Net and selective feature extraction for land cover classification using remote sensing imagery
In this study, we explore an enhancement to the U-Net architecture by integrating SK-
ResNeXt as the encoder for Land Cover Classification (LCC) tasks using Multispectral …
ResNeXt as the encoder for Land Cover Classification (LCC) tasks using Multispectral …
Nested DWT–Based CNN Architecture for Monocular Depth Estimation
Applications such as medical diagnosis, navigation, robotics, etc., require 3D images.
Recently, deep learning networks have been extensively applied to estimate depth. Depth …
Recently, deep learning networks have been extensively applied to estimate depth. Depth …
RWKV-UNet: Improving UNet with Long-Range Cooperation for Effective Medical Image Segmentation
In recent years, there have been significant advancements in deep learning for medical
image analysis, especially with convolutional neural networks (CNNs) and transformer …
image analysis, especially with convolutional neural networks (CNNs) and transformer …
BCNet: integrating UNet and transformer for blood cell segmentation
Y Jiang, S Wang, M Yao, Q **ao, Y Li, H Bai… - Signal, Image and Video …, 2025 - Springer
Automatic segmentation of blood cells is crucial in medical diagnosis and research,
significantly improving the accuracy and efficiency of diagnosing blood disorders. Traditional …
significantly improving the accuracy and efficiency of diagnosing blood disorders. Traditional …