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https://doi.org/10.2139/ssrn.4664190

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inferredcites →https://doi.org/10.1002/rob.20122openalex-extracted
A multidegree-of-freedom manipulator for curtain-wall installation
inferredcites →https://doi.org/10.1007/978-3-030-01234-2_1openalex-extracted
CBAM: Convolutional Block Attention Module
inferredcites →https://doi.org/10.1007/978-3-030-01234-2_49openalex-extracted
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
inferredcites →https://doi.org/10.1007/978-3-031-43901-8_28openalex-extracted
Boundary Difference over Union Loss for Medical Image Segmentation
inferredcites →https://doi.org/10.1007/978-3-319-24574-4_28openalex-extracted
U-Net: Convolutional Networks for Biomedical Image Segmentation
inferredcites →https://doi.org/10.1007/s10514-006-9019-2openalex-extracted
Development of the curtain wall installation robot: Performance and efficiency tests at a construction site
inferredcites →https://doi.org/10.1016/j.aei.2020.101206openalex-extracted
Automatic defect detection of metro tunnel surfaces using a vision-based inspection system
inferredcites →https://doi.org/10.1016/j.autcon.2007.08.006openalex-extracted
Power assist devices for installing plaster panels in construction
inferredcites →https://doi.org/10.1016/j.autcon.2013.09.006openalex-extracted
Life cycle design and prefabrication in buildings: A review and case studies in Hong Kong
inferredcites →https://doi.org/10.1016/j.autcon.2019.01.006openalex-extracted
Trajectory adaptation for an impedance controlled cooperative robot according to an operator's force
inferredcites →https://doi.org/10.1016/j.autcon.2021.103685openalex-extracted
Automatic segmentation of TBM muck images via a deep-learning approach to estimate the size and shape of rock chips
inferredcites →https://doi.org/10.1016/j.autcon.2021.104106openalex-extracted
Automatic segmentation of concrete aggregate using convolutional neural network
inferredcites →https://doi.org/10.1016/j.buildenv.2011.09.010openalex-extracted
Comparative assessment of life cycle impacts of curtain wall mullions
inferredcites →https://doi.org/10.1016/j.compind.2021.103448openalex-extracted
Utilizing safety rule correlation for mobile scaffolds monitoring leveraging deep convolution neural networks
inferredcites →https://doi.org/10.1016/j.compind.2021.103545openalex-extracted
Pixel-level tunnel crack segmentation using a weakly supervised annotation approach
inferredcites →https://doi.org/10.1016/j.compind.2022.103698openalex-extracted
MiniCrack: A simple but efficient convolutional neural network for pixel-level narrow crack detection
inferredcites →https://doi.org/10.1016/j.compind.2022.103720openalex-extracted
WaferSegClassNet - A light-weight network for classification and segmentation of semiconductor wafer defects
inferredcites →https://doi.org/10.1016/j.compind.2023.103901openalex-extracted
Few-shot semantic segmentation for industrial defect recognition
inferredcites →https://doi.org/10.1016/j.jobe.2021.102445openalex-extracted
Slim curtain wall spandrel integrated with vacuum insulation panel: A state-of-the-art review and future opportunities
inferredcites →https://doi.org/10.1109/3dv.2016.79openalex-extracted
V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
inferredcites →https://doi.org/10.1109/cvpr.2015.7298965openalex-extracted
Fully convolutional networks for semantic segmentation
inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-extracted
Deep Residual Learning for Image Recognition
inferredcites →https://doi.org/10.1109/cvpr.2017.189openalex-extracted
Large Kernel Matters — Improve Semantic Segmentation by Global Convolutional Network
inferredcites →https://doi.org/10.1109/cvpr.2017.549openalex-extracted
RefineNet: Multi-path Refinement Networks for High-Resolution Semantic Segmentation
inferredcites →https://doi.org/10.1109/cvpr.2017.660openalex-extracted
Pyramid Scene Parsing Network
inferredcites →https://doi.org/10.1109/cvpr42600.2020.01155openalex-extracted
ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks
inferredcites →https://doi.org/10.1109/iccv.2015.169openalex-extracted
Fast R-CNN
inferredcites →https://doi.org/10.1109/iccv48922.2021.00009openalex-extracted
CvT: Introducing Convolutions to Vision Transformers
inferredcites →https://doi.org/10.1109/tim.2022.3178991openalex-extracted
DS-TransUNet: Dual Swin Transformer U-Net for Medical Image Segmentation
inferredcites →https://doi.org/10.1109/tits.2022.3154407openalex-extracted
External Attention Based TransUNet and Label Expansion Strategy for Crack Detection
inferredcites →https://doi.org/10.1109/tpami.2016.2644615openalex-extracted
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
inferredcites →https://doi.org/10.1109/tpami.2017.2699184openalex-extracted
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
inferredcites →https://doi.org/10.1109/tpami.2022.3202765openalex-extracted
P2T: Pyramid Pooling Transformer for Scene Understanding
inferredcites →https://doi.org/10.1109/wacv48630.2021.00407openalex-extracted
Multi-Modal Reasoning Graph for Scene-Text Based Fine-Grained Image Classification and Retrieval
inferredcites →https://doi.org/10.1111/mice.12626openalex-extracted
Automated crack assessment and quantitative growth monitoring
inferredcites →https://doi.org/10.1111/mice.12699openalex-extracted
Automated extraction of structural beam lines and connections from point clouds of steel buildings
inferredcites →https://doi.org/10.48550/arxiv.1411.4038openalex-extracted
Fully Convolutional Networks for Semantic Segmentation
inferredcites →https://doi.org/10.48550/arxiv.1802.02611openalex-extracted
inferredcites →https://doi.org/10.48550/arxiv.1804.03999openalex-extracted
Attention U-Net: Learning Where to Look for the Pancreas
inferredcites →https://doi.org/10.48550/arxiv.1910.03151openalex-extracted
inferredcites →https://doi.org/10.48550/arxiv.2108.05895openalex-extracted
inferredcites →https://doi.org/10.48550/arxiv.2204.12511openalex-extracted
PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions
inferredcites →https://doi.org/10.48550/arxiv.2308.00220openalex-extracted
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