Understanding Convolution for Semantic Segmentation
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- Type
- article
- Published
- 2017-02-27
- Cited by
- 1,923
- References
- 49
- Access
- Open access
- OpenAlex
- https://openalex.org/W2592939477
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:4599765
Keywords
Computer science, Upsampling, Segmentation, Artificial intelligence, Pascal (unit)
References
- FlowNet: Learning Optical Flow with Convolutional Networks
- High-for-Low and Low-for-High: Efficient Boundary Detection from Deep Object Features and Its Applications to High-Level Vision
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- Learning Deconvolution Network for Semantic Segmentation
- Learning to generate chairs with convolutional neural networks
- Fully convolutional networks for semantic segmentation
- SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
- Hypercolumns for object segmentation and fine-grained localization
- Modeling local and global deformations in Deep Learning: Epitomic convolution, Multiple Instance Learning, and sliding window detection
- Going deeper with convolutions
- Fully Connected Deep Structured Networks
- ImageNet Large Scale Visual Recognition Challenge
- Conditional Random Fields as Recurrent Neural Networks
- Adaptive deconvolutional networks for mid and high level feature learning
- Semantic contours from inverse detectors
- Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials
- ImageNet classification with deep convolutional neural networks
- A new performance measure and evaluation benchmark for road detection algorithms
- Higher Order Potentials in End-to-End Trainable Conditional Random Fields
- ABC-CNN: An Attention Based Convolutional Neural Network for Visual Question Answering
Cited by
- Deep Crisp Boundaries: From Boundaries to Higher-Level Tasks
- Heart rate estimation from ballistocardiogram signals processing via low-cost telemedicine architectures: a comparative performance evaluation
- Predicting the Driver's Focus of Attention: The DR(eye)VE Project
- Towards The Deep Model: Understanding Visual Recognition Through Computational Models
- Dilated Residual Networks
- Rethinking Atrous Convolution for Semantic Image Segmentation
- Semantic Video CNNs Through Representation Warping
- MIT Advanced Vehicle Technology Study: Large-Scale Naturalistic Driving Study of Driver Behavior and Interaction With Automation
- Ladder-Style DenseNets for Semantic Segmentation of Large Natural Images
- Melanoma Diagnostics Using Fully Convolutional Networks on Whole Slide Images
- In-place Activated BatchNorm for Memory-Optimized Training of DNNs
- MaskLab: Instance Segmentation by Refining Object Detection with Semantic and Direction Features
- Scale-Adaptive Convolutions for Scene Parsing
- The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes
- Restricted Deformable Convolution-Based Road Scene Semantic Segmentation Using Surround View Cameras
- Scene segmentation with deep neural networks
- Fully Convolutional Network Based Skeletonization for Handwritten Chinese Characters
- ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation
- Deep fully convolutional networks with random data augmentation for enhanced generalization in road detection
- Video Object Segmentation with Language Referring Expressions
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