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Quantized Semantic Segmentation Deep Architecture for Deployment on an Edge Computing Device for Image Segmentation

https://doi.org/10.3390/electronics11213561

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Recurrent Neural Networks

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Filter-Wise Pruning Approach to FPGA Implementation of Fully Convolutional Network for Semantic Segmentation

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Fast Semantic Segmentation of RGB-D Scenes with GPU-Accelerated Deep Neural Networks

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U-Net: Convolutional Networks for Biomedical Image Segmentation

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XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks

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3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation

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RETRACTED ARTICLE: Medical image semantic segmentation based on deep learning

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A privacy-aware method for COVID-19 detection in chest CT images using lightweight deep conventional neural network and blockchain

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Electrodeposition of copper on titanium wires: Taguchi experimental design approach

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Holistic decomposition convolution for effective semantic segmentation of medical volume images

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Volumetric memory network for interactive medical image segmentation

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Road segmentation for all-day outdoor robot navigation

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Image Segmentation Using K -means Clustering Algorithm and Subtractive Clustering Algorithm

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FPGA Implementation of 3-bit Quantized CNN for Semantic Segmentation

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Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state

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Convergence of Edge Computing and Deep Learning: A Comprehensive Survey

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Semantic texton forests for image categorization and segmentation

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Fully convolutional networks for semantic segmentation

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Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference

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Rethinking Semantic Segmentation: A Prototype View

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ReSeg: A Recurrent Neural Network-Based Model for Semantic Segmentation

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A Deep Learning Method Combined Sparse Autoencoder with SVM

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Learning Deconvolution Network for Semantic Segmentation

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Weakly-and Semi-Supervised Learning of a Deep Convolutional Network for Semantic Image Segmentation

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Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-scale Convolutional Architecture

inferredcites →https://doi.org/10.1109/iccv48922.2021.00717openalex-snapshot

Segmenter: Transformer for Semantic Segmentation

inferredcites →https://doi.org/10.1109/iccv48922.2021.00721openalex-snapshot

Exploring Cross-Image Pixel Contrast for Semantic Segmentation

inferredcites →https://doi.org/10.1109/iros.2009.5354084openalex-snapshot

Scalable learning for object detection with GPU hardware

inferredcites →https://doi.org/10.1109/ivs.2018.8500504openalex-snapshot

MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving

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Combination of computer vision detection and segmentation for autonomous driving

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A Comparison of FPGA and GPU for Real-Time Phase-Based Optical Flow, Stereo, and Local Image Features

inferredcites →https://doi.org/10.1109/tpami.2016.2644615openalex-snapshot

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

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Deep High-Resolution Representation Learning for Visual Recognition

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Understanding Convolution for Semantic Segmentation

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UNETR: Transformers for 3D Medical Image Segmentation

inferredcites →https://doi.org/10.1117/12.2288713openalex-snapshot

Real-time object detection and semantic segmentation for autonomous driving

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Multi-class image segmentation using conditional random fields and global classification

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Efficient Acceleration of CNNs for Semantic Segmentation on FPGAs

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Minimum Porosity Formation in Pressure Die Casting by Taguchi Method

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Optimization of the Physical and Mechanical Properties of a Spline Surface Fabricated by High-Speed Cold Roll Beating Based on Taguchi Theory

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How to Train a Compact Binary Neural Network with High Accuracy?

inferredcites →https://doi.org/10.24963/ijcai.2017/208openalex-snapshot

Importance-Aware Semantic Segmentation for Autonomous Driving System

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Deep Q-Learning Technique for Offloading Offline/Online Computation in Blockchain-Enabled Green IoT-Edge Scenarios

inferredcites →https://doi.org/10.3390/electronics10222823openalex-snapshot

Integer-Only CNNs with 4 Bit Weights and Bit-Shift Quantization Scales at Full-Precision Accuracy

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A Systematic Literature Review on Distributed Machine Learning in Edge Computing

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Towards Accurate Binary Convolutional Neural Network

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Object Class Segmentation using Random Forests

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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A comprehensive review of efficient deep learning for clinical medical imaging deployment

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A global-frequency-domain network for medical image segmentation

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Optimizing Semantic Segmentation for Enhanced Football Analytics: A Pixel-level Approach

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FPGA-Based Batik Classification Using Quantization Aware Training of MobileNet and Data-flow Implementation

inferred← citeshttps://doi.org/10.1109/nmitcon58196.2023.10275893openalex-snapshot

Image Instance Segmentation and Optimization Using Quantization for Embedded Devices

inferred← citeshttps://doi.org/10.1109/tgcn.2023.3321113openalex-snapshot

Building Energy Efficient Semantic Segmentation in Intelligent Edge Computing

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PMNet: a multi-branch and multi-scale semantic segmentation approach to water extraction from high-resolution remote sensing images with edge-cloud computing

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An Efficient Framework for Accurate Liver Segmentation in Abdominal CT Images with Low Knowledge Requirement

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Deep Learning Architecture Improvement Based on Dynamic Pruning and Layer Fusion

inferred← citeshttps://doi.org/10.3390/technologies14010076openalex-snapshot

Quantization of Deep Neural Networks for Medical Image Analysis: A Systematic Review and Meta-Analysis

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