Semantic Segmentation
5086 papers with code • 120 benchmarks • 303 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 )
Libraries
Use these libraries to find Semantic Segmentation models and implementationsSubtasks
- Tumor Segmentation
- Panoptic Segmentation
- 3D Semantic Segmentation
- Weakly-Supervised Semantic Segmentation
- Weakly-Supervised Semantic Segmentation
- Scene Segmentation
- Semi-Supervised Semantic Segmentation
- Real-Time Semantic Segmentation
- 3D Part Segmentation
- Unsupervised Semantic Segmentation
- Road Segmentation
- One-Shot Segmentation
- Bird's-Eye View Semantic Segmentation
- Crack Segmentation
- Universal Segmentation
- Class-Incremental Semantic Segmentation
- UNET Segmentation
- Polyp Segmentation
- Vision-Language Segmentation
- 4D Spatio Temporal Semantic Segmentation
- Histopathological Segmentation
- Attentive segmentation networks
- Text-Line Extraction
- Aerial Video Semantic Segmentation
- Amodal Panoptic Segmentation
- Robust BEV Map Segmentation
Latest papers with no code
Annolid: Annotate, Segment, and Track Anything You Need
Annolid is a deep learning-based software package designed for the segmentation, labeling, and tracking of research targets within video files, focusing primarily on animal behavior analysis.
Generating Diverse Agricultural Data for Vision-Based Farming Applications
The integration of real-world textures and environmental factors into the procedural generation process enhances the photorealism and applicability of the synthetic data.
A Quantum Fuzzy-based Approach for Real-Time Detection of Solar Coronal Holes
The task has been carried out in two stages, in first stage the solar image has been segmented using a quantum computing based fast fuzzy c-mean (QCFFCM) and in the later stage the CHs has been extracted out from the segmented image based on image morphological operation.
I2CKD : Intra- and Inter-Class Knowledge Distillation for Semantic Segmentation
Consequently, I2CKD enables the student to better mimic the feature representation of the teacher for each class, thereby enhancing the segmentation performance of the compact network.
Deep Learning Segmentation and Classification of Red Blood Cells Using a Large Multi-Scanner Dataset
In this paper, we report a new large red blood cell (RBC) image dataset and propose a two-stage deep learning framework for RBC image segmentation and classification.
ViTAR: Vision Transformer with Any Resolution
Firstly, we propose a novel module for dynamic resolution adjustment, designed with a single Transformer block, specifically to achieve highly efficient incremental token integration.
Road Obstacle Detection based on Unknown Objectness Scores
However, when applied to images with many unknowns and complex components, such as driving scenes, these methods often exhibit unstable performance.
ReMamber: Referring Image Segmentation with Mamba Twister
Referring Image Segmentation (RIS) leveraging transformers has achieved great success on the interpretation of complex visual-language tasks.
CT Synthesis with Conditional Diffusion Models for Abdominal Lymph Node Segmentation
To address these problems, we present a pipeline that integrates the conditional diffusion model for lymph node generation and the nnU-Net model for lymph node segmentation to improve the segmentation performance of abdominal lymph nodes through synthesizing a diversity of realistic abdominal lymph node data.
Rotate to Scan: UNet-like Mamba with Triplet SSM Module for Medical Image Segmentation
Image segmentation holds a vital position in the realms of diagnosis and treatment within the medical domain.