2D Semantic Segmentation
38 papers with code • 9 benchmarks • 57 datasets
Benchmarks
These leaderboards are used to track progress in 2D Semantic Segmentation
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Datasets
Cityscapes
CamVid
KITTI-360
xBD
KITTI MOTS
SpaceNet 7
WaterScenes
DeepSport Dataset
Subtasks
Most implemented papers
Self-Improving Semantic Perception for Indoor Localisation
ethz-asl/background_foreground_segmentation
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We find memory replay an effective measure to reduce forgetting and show how the robotic system can improve even when switching between different environments.
Semi-Supervised Segmentation of Multi-vendor and Multi-center Cardiac MRI
Finally, we applied our method to two benchmark datasets, STACOM2018, and M&Ms 2020 challenges, to show the potency of the proposed model.
BDANet: Multiscale Convolutional Neural Network with Cross-directional Attention for Building Damage Assessment from Satellite Images
ShaneShen/BDANet-Building-Damage-Assessment
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With a pair of pre- and post-disaster satellite images, building damage assessment aims at predicting the extent of damage to buildings.
Segmentation of Drilled Holes in Texture Wooden Furniture Panels Using Deep Neural Network
rytisss/PanelsDrillSegmentation
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MDPI Sensors 2021
Drilling operations are an essential part of furniture from MDF laminated boards required for product assembly.
DeepIndices: Remote Sensing Indices Based on Approximation of Functions through Deep-Learning, Application to Uncalibrated Vegetation Images
phd-thesis-adventice/phd-index-optimizer-tensorflow
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Remote Sensing 2021
The objective of this study is to develop a method to find the optimal index, using a statistical approach by gradient descent on different forms of generic equations.
A modular U-Net for automated segmentation of X-ray tomography images in composite materials
X-ray Computed Tomography (XCT) techniques have evolved to a point that high-resolution data can be acquired so fast that classic segmentation methods are prohibitively cumbersome, demanding automated data pipelines capable of dealing with non-trivial 3D images.
Hierarchical Representations and Explicit Memory: Learning Effective Navigation Policies on 3D Scene Graphs using Graph Neural Networks
mit-tesse/dsg-rl
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In this work, we present a reinforcement learning framework that leverages high-level hierarchical representations to learn navigation policies.
Satellite Image Semantic Segmentation
koechslin/swin-transformer-semantic-segmentation
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In this paper, we propose a method for the automatic semantic segmentation of satellite images into six classes (sparse forest, dense forest, moor, herbaceous formation, building, and road).
Automating cell counting in fluorescent microscopy through deep learning with c-ResUnet
Counting cells in fluorescent microscopy is a tedious, time-consuming task that researchers have to accomplish to assess the effects of different experimental conditions on biological structures of interest.
Segmentation-Based vs. Regression-Based Biomarker Estimation: A Case Study of Fetus Head Circumference Assessment from Ultrasound Images
jizhang02/HC-reg-seg
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Journal of Imaging 2022
Even if this type of segmentation-free approaches have been boosted with deep learning, it is not yet clear how well direct approach can compare to segmentation approaches, which are expected to be still more accurate.
