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Attfire: Attention-Enhanced Fire Modules for Image Segmentation
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Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-3-030-01234-2_1openalex-snapshot
CBAM: Convolutional Block Attention Module
inferredcites →https://doi.org/10.1007/978-3-030-01234-2_48openalex-snapshot
AMC: AutoML for Model Compression and Acceleration on Mobile Devices
inferredcites →https://doi.org/10.1007/978-3-031-72114-4_47openalex-snapshot
nnU-Net Revisited: A Call for Rigorous Validation in 3D Medical Image Segmentation
inferredcites →https://doi.org/10.1007/978-3-319-10602-1_48openalex-snapshot
Microsoft COCO: Common Objects in Context
inferredcites →https://doi.org/10.1007/978-3-319-24574-4_28openalex-snapshot
U-Net: Convolutional Networks for Biomedical Image Segmentation
inferredcites →https://doi.org/10.1016/j.autcon.2021.103874openalex-snapshot
Automated semantic segmentation of industrial point clouds using ResPointNet++
inferredcites →https://doi.org/10.1016/j.compbiomed.2024.109353openalex-snapshot
LATUP-Net: A lightweight 3D attention U-Net with parallel convolutions for brain tumor segmentation
inferredcites →https://doi.org/10.1016/j.neucom.2024.128740openalex-snapshot
A comprehensive review of deep learning for medical image segmentation
inferredcites →https://doi.org/10.1016/j.neunet.2020.09.006openalex-snapshot
Hybrid tensor decomposition in neural network compression
inferredcites →https://doi.org/10.1016/j.patcog.2020.107538openalex-snapshot
DecomVQANet: Decomposing visual question answering deep network via tensor decomposition and regression
inferredcites →https://doi.org/10.1109/access.2019.2949076openalex-snapshot
Efficient Semantic Segmentation Using Spatio-Channel Dilated Convolutions
inferredcites →https://doi.org/10.1109/cvpr.2015.7298965openalex-snapshot
Fully convolutional networks for semantic segmentation
inferredcites →https://doi.org/10.1109/cvpr.2017.195openalex-snapshot
Xception: Deep Learning with Depthwise Separable Convolutions
inferredcites →https://doi.org/10.1109/cvpr.2018.00286openalex-snapshot
Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference
inferredcites →https://doi.org/10.1109/cvpr.2018.00716openalex-snapshot
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
inferredcites →https://doi.org/10.1109/cvpr.2018.00747openalex-snapshot
Context Encoding for Semantic Segmentation
inferredcites →https://doi.org/10.1109/cvpr42600.2020.01155openalex-snapshot
ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks
inferredcites →https://doi.org/10.1109/cvpr42600.2020.01461openalex-snapshot
Rethinking Depthwise Separable Convolutions: How Intra-Kernel Correlations Lead to Improved MobileNets
inferredcites →https://doi.org/10.1109/cvpr46437.2021.01350openalex-snapshot
Coordinate Attention for Efficient Mobile Network Design
inferredcites →https://doi.org/10.1109/iccv.2017.89openalex-snapshot
Deformable Convolutional Networks
inferredcites →https://doi.org/10.1109/iccv.2019.00339openalex-snapshot
MetaPruning: Meta Learning for Automatic Neural Network Channel Pruning
inferredcites →https://doi.org/10.1109/iccvw.2017.71openalex-snapshot
Factorized Convolutional Neural Networks
inferredcites →https://doi.org/10.1109/tmi.2018.2867261openalex-snapshot
Recalibrating Fully Convolutional Networks With Spatial and Channel “Squeeze and Excitation” Blocks
inferredcites →https://doi.org/10.1109/tpami.2017.2699184openalex-snapshot
DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
inferredcites →https://doi.org/10.20517/ir.2025.09openalex-snapshot
An overview of industrial image segmentation using deep learning models
inferredcites →https://doi.org/10.2139/ssrn.4370101openalex-snapshot
Cross-Cbam: A Lightweight Network for Scene Segmentation
inferredcites →https://doi.org/10.4230/lipics.ecrts.2024.13openalex-snapshot
DeepTrust^RT: Confidential Deep Neural Inference Meets Real-Time!
inferredcites →https://doi.org/10.48550/arxiv.1404.0736openalex-snapshot
Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation
inferredcites →https://doi.org/10.48550/arxiv.1411.4038openalex-snapshot
Fully Convolutional Networks for Semantic Segmentation
inferredcites →https://doi.org/10.48550/arxiv.1503.02531openalex-snapshot
Distilling the Knowledge in a Neural Network
inferredcites →https://doi.org/10.48550/arxiv.1506.04579openalex-snapshot
ParseNet: Looking Wider to See Better
inferredcites →https://doi.org/10.48550/arxiv.1602.07360openalex-snapshot
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
inferredcites →https://doi.org/10.48550/arxiv.1709.01507openalex-snapshot
Squeeze-and-Excitation Networks
inferredcites →https://doi.org/10.48550/arxiv.2207.12939openalex-snapshot
Semantic Segmentation for Autonomous Driving: Model Evaluation, Dataset Generation, Perspective Comparison, and Real-Time Capability
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