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Importance-Aware Semantic Segmentation for Autonomous Vehicles

https://doi.org/10.1109/tits.2018.2801309

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What this work says

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

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A Subjective Method for Image Segmentation Evaluation

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What, Where and How Many? Combining Object Detectors and CRFs

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Semantic Segmentation of Urban Scenes Using Dense Depth Maps

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Indoor Segmentation and Support Inference from RGBD Images

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Unsupervised segmentation of color-texture regions in images and video

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Semantic texton forests for image categorization and segmentation

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RGB-(D) scene labeling: Features and algorithms

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Fully convolutional networks for semantic segmentation

inferredcites →https://doi.org/10.1109/cvpr.2016.28openalex-snapshot

Object Contour Detection with a Fully Convolutional Encoder-Decoder Network

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The Cityscapes Dataset for Semantic Urban Scene Understanding

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Deep Residual Learning for Image Recognition

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Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes

inferredcites →https://doi.org/10.1109/cvpr.2017.684openalex-snapshot

Not All Pixels Are Equal: Difficulty-Aware Semantic Segmentation via Deep Layer Cascade

inferredcites →https://doi.org/10.1109/cvpr.2017.749openalex-snapshot

Loss Max-Pooling for Semantic Image Segmentation

inferredcites →https://doi.org/10.1109/cvprw.2014.80openalex-snapshot

Unrolling Loopy Top-Down Semantic Feedback in Convolutional Deep Networks

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Structured class-labels in random forests for semantic image labelling

inferredcites →https://doi.org/10.1109/iccv.2015.178openalex-snapshot

Learning Deconvolution Network for Semantic Segmentation

inferredcites →https://doi.org/10.1109/iccv.2015.179openalex-snapshot

Conditional Random Fields as Recurrent Neural Networks

inferredcites →https://doi.org/10.1109/iccv.2015.304openalex-snapshot

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

inferredcites →https://doi.org/10.1109/itsc.2014.6957660openalex-snapshot

Design and implementation of driving control system for autonomous vehicle

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Efficient ConvNet for real-time semantic segmentation

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Road Scene Content Analysis for Driver Assistance and Autonomous Driving

inferredcites →https://doi.org/10.1109/tits.2017.2702012openalex-snapshot

Cross-Domain Traffic Scene Understanding: A Dense Correspondence-Based Transfer Learning Approach

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ERFNet: Efficient Residual Factorized ConvNet for Real-Time Semantic Segmentation

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Statistical region merging

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

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Automatic Portrait Segmentation for Image Stylization

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Importance-Aware Semantic Segmentation for Autonomous Driving System

inferredcites →https://doi.org/10.48550/arxiv.1202.2160openalex-snapshot

Scene Parsing with Multiscale Feature Learning, Purity Trees, and Optimal Covers

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Very Deep Convolutional Networks for Large-Scale Image Recognition

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Adam: A Method for Stochastic Optimization

inferredcites →https://doi.org/10.48550/arxiv.1412.7062openalex-snapshot

Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs

inferredcites →https://doi.org/10.48550/arxiv.1502.03167openalex-snapshot

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

inferredcites →https://doi.org/10.48550/arxiv.1506.04924openalex-snapshot

Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation

inferredcites →https://doi.org/10.48550/arxiv.1606.02147openalex-snapshot

ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation

inferredcites →https://doi.org/10.5244/c.23.62openalex-snapshot

Combining Appearance and Structure from Motion Features for Road Scene Understanding

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Scene Text Recognition using Higher Order Language Priors

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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

What points at this work

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Exploring Uncertainty-Based Self-Prompt for Test-Time Adaptation Semantic Segmentation in Remote Sensing Images

inferred← citeshttps://doi.org/10.3390/s21093270openalex-snapshot

Multi-Level and Multi-Scale Feature Aggregation Network for Semantic Segmentation in Vehicle-Mounted Scenes

inferred← citeshttps://doi.org/10.3390/s22072654openalex-snapshot

Recent Advances in Vision-Based On-Road Behaviors Understanding: A Critical Survey

inferred← citeshttps://doi.org/10.3390/s26165035openalex-snapshot

Task-Oriented Deep Joint Source-Channel Coding with Semantic-Aware Adaptive Quantization for Autonomous Driving

inferred← citeshttps://doi.org/10.48550/arxiv.2007.12869openalex-snapshot

Applying Semantic Segmentation to Autonomous Cars in the Snowy Environment

inferred← citeshttps://doi.org/10.48550/arxiv.2008.04751openalex-snapshot

Reinforced Wasserstein Training for Severity-Aware Semantic Segmentation in Autonomous Driving

inferred← citeshttps://doi.org/10.5204/thesis.eprints.229733openalex-snapshot

Performance monitoring of deep learning vision systems during deployment

inferred← citeshttps://doi.org/10.70675/d65e21c5z8009z4ce9z8599z9cf93a88da18openalex-snapshot

Apprentissage profond interactif et semi-supervisé pour la segmentation volumique en tomographie électronique

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