Semantic Segmentation
5199 papers with code • 125 benchmarks • 311 datasets
Semantic Segmentation is a computer vision task in which the goal is to categorize each pixel in an image into a class or object. The goal is to produce a dense pixel-wise segmentation map of an image, where each pixel is assigned to a specific class or object. Some example benchmarks for this task are Cityscapes, PASCAL VOC and ADE20K. Models are usually evaluated with the Mean Intersection-Over-Union (Mean IoU) and Pixel Accuracy metrics.
( Image credit: CSAILVision )
Benchmarks
These leaderboards are used to track progress in Semantic Segmentation
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Libraries
Use these libraries to find Semantic Segmentation models and implementations
MS COCO
Cityscapes
KITTI
ShapeNet
ScanNet
ADE20K
NYUv2
DAVIS
SYNTHIA
EuroSAT
Subtasks
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Tumor Segmentation
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Panoptic Segmentation
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3D Semantic Segmentation
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Weakly-Supervised Semantic Segmentation
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Weakly-Supervised Semantic Segmentation
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Scene Segmentation
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Semi-Supervised Semantic Segmentation
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Real-Time Semantic Segmentation
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3D Part Segmentation
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Unsupervised Semantic Segmentation
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Road Segmentation
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One-Shot Segmentation
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Bird's-Eye View Semantic Segmentation
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Crack Segmentation
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UNET Segmentation
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Universal Segmentation
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Class-Incremental Semantic Segmentation
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Polyp Segmentation
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Vision-Language Segmentation
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4D Spatio Temporal Semantic Segmentation
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Histopathological Segmentation
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Attentive segmentation networks
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Text-Line Extraction
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Aerial Video Semantic Segmentation
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Amodal Panoptic Segmentation
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Robust BEV Map Segmentation
Latest papers
Learning from Unlabelled Data with Transformers: Domain Adaptation for Semantic Segmentation of High Resolution Aerial Images
esa-philab/learning_from_unlabeled_data_for_domain_adaptation_for_semantic_segmentation
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In this paper, we develop a new model for semantic segmentation of unlabelled images, the Non-annotated Earth Observation Semantic Segmentation (NEOS) model.
Boosting Medical Image Segmentation Performance with Adaptive Convolution Layer
Medical image segmentation plays a vital role in various clinical applications, enabling accurate delineation and analysis of anatomical structures or pathological regions.
Mushroom Segmentation and 3D Pose Estimation from Point Clouds using Fully Convolutional Geometric Features and Implicit Pose Encoding
We have validated the effectiveness of the proposed implicit-based approach for a synthetic test set, as well as provided qualitative results for a small set of real acquired point clouds with depth sensors.
Learning Feature Inversion for Multi-class Anomaly Detection under General-purpose COCO-AD Benchmark
zhangzjn/ader
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Moreover, current metrics such as AU-ROC have nearly reached saturation on simple datasets, which prevents a comprehensive evaluation of different methods.
ECLAIR: A High-Fidelity Aerial LiDAR Dataset for Semantic Segmentation
sharpershape/eclair-dataset
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We introduce ECLAIR (Extended Classification of Lidar for AI Recognition), a new outdoor large-scale aerial LiDAR dataset designed specifically for advancing research in point cloud semantic segmentation.
A Concise Tiling Strategy for Preserving Spatial Context in Earth Observation Imagery
We propose a new tiling strategy, Flip-n-Slide, which has been developed for specific use with large Earth observation satellite images when the location of objects-of-interest (OoI) is unknown and spatial context can be necessary for class disambiguation.
Vocabulary-free Image Classification and Semantic Segmentation
altndrr/vicss
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To address VIC, we propose Category Search from External Databases (CaSED), a training-free method that leverages a pre-trained vision-language model and an external database.
Gasformer: A Transformer-based Architecture for Segmenting Methane Emissions from Livestock in Optical Gas Imaging
Methane emissions from livestock, particularly cattle, significantly contribute to climate change.
Learnable Prompt for Few-Shot Semantic Segmentation in Remote Sensing Domain
SteveImmanuel/OEM-Few-Shot-Learnable-Prompt
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Few-shot segmentation is a task to segment objects or regions of novel classes within an image given only a few annotated examples.
nnU-Net Revisited: A Call for Rigorous Validation in 3D Medical Image Segmentation
MIC-DKFZ/nnunet
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The release of nnU-Net marked a paradigm shift in 3D medical image segmentation, demonstrating that a properly configured U-Net architecture could still achieve state-of-the-art results.
