Semantic Segmentation
5164 papers with code • 125 benchmarks • 313 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
ViM-UNet: Vision Mamba for Biomedical Segmentation
Here, we introduce ViM-UNet, a novel segmentation architecture based on it and compare it to UNet and UNETR for two challenging microscopy instance segmentation tasks.
OpenTrench3D: A Photogrammetric 3D Point Cloud Dataset for Semantic Segmentation of Underground Utilities
We present OpenTrench3D, a novel and comprehensive 3D Semantic Segmentation point cloud dataset, designed to advance research and development in underground utility surveying and mapping.
Multi-view Aggregation Network for Dichotomous Image Segmentation
Dichotomous Image Segmentation (DIS) has recently emerged towards high-precision object segmentation from high-resolution natural images.
Rethinking the Spatial Inconsistency in Classifier-Free Diffusion Guidance
Classifier-Free Guidance (CFG) has been widely used in text-to-image diffusion models, where the CFG scale is introduced to control the strength of text guidance on the whole image space.
LHU-Net: A Light Hybrid U-Net for Cost-Efficient, High-Performance Volumetric Medical Image Segmentation
As a result of the rise of Transformer architectures in medical image analysis, specifically in the domain of medical image segmentation, a multitude of hybrid models have been created that merge the advantages of Convolutional Neural Networks (CNNs) and Transformers.
FPL+: Filtered Pseudo Label-based Unsupervised Cross-Modality Adaptation for 3D Medical Image Segmentation
Adapting a medical image segmentation model to a new domain is important for improving its cross-domain transferability, and due to the expensive annotation process, Unsupervised Domain Adaptation (UDA) is appealing where only unlabeled images are needed for the adaptation.
Frequency Decomposition-Driven Unsupervised Domain Adaptation for Remote Sensing Image Semantic Segmentation
Cross-domain semantic segmentation of remote sensing (RS) imagery based on unsupervised domain adaptation (UDA) techniques has significantly advanced deep-learning applications in the geosciences.
Sigma: Siamese Mamba Network for Multi-Modal Semantic Segmentation
In this work, we introduce Sigma, a Siamese Mamba network for multi-modal semantic segmentation, utilizing the Selective Structured State Space Model, Mamba.
PARIS3D: Reasoning-based 3D Part Segmentation Using Large Multimodal Model
We introduce a novel segmentation task known as reasoning part segmentation for 3D objects, aiming to output a segmentation mask based on complex and implicit textual queries about specific parts of a 3D object.
HAPNet: Toward Superior RGB-Thermal Scene Parsing via Hybrid, Asymmetric, and Progressive Heterogeneous Feature Fusion
In this study, we take one step toward this new research area by exploring a feasible strategy to fully exploit VFM features for RGB-thermal scene parsing.
