Semantic Segmentation
6002 papers with code • 147 benchmarks • 332 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
- Crack Segmentation
- One-Shot Segmentation
- Bird's-Eye View Semantic Segmentation
- Universal Segmentation
- UNET Segmentation
- Class-Incremental Semantic Segmentation
- Polyp Segmentation
- Vision-Language Segmentation
- Flood extent forecasting
- Histopathological Segmentation
- Speech Prompted Semantic Segmentation
- Sound Prompted Semantic Segmentation
- 4D Spatio Temporal Semantic Segmentation
- Attentive segmentation networks
- Text-Line Extraction
- Aerial Video Semantic Segmentation
- Amodal Panoptic Segmentation
- Robust BEV Map Segmentation
Most implemented papers
U-Net: Convolutional Networks for Biomedical Image Segmentation
There is large consent that successful training of deep networks requires many thousand annotated training samples.
Deep Residual Learning for Image Recognition
Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.
Mask R-CNN
Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance.
MobileNetV2: Inverted Residuals and Linear Bottlenecks
In this paper we describe a new mobile architecture, MobileNetV2, that improves the state of the art performance of mobile models on multiple tasks and benchmarks as well as across a spectrum of different model sizes.
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
While the Transformer architecture has become the de-facto standard for natural language processing tasks, its applications to computer vision remain limited.
MMDetection: Open MMLab Detection Toolbox and Benchmark
In this paper, we introduce the various features of this toolbox.
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Point cloud is an important type of geometric data structure.
FCOS: Fully Convolutional One-Stage Object Detection
By eliminating the predefined set of anchor boxes, FCOS completely avoids the complicated computation related to anchor boxes such as calculating overlapping during training.
Feature Pyramid Networks for Object Detection
Feature pyramids are a basic component in recognition systems for detecting objects at different scales.
Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
The former networks are able to encode multi-scale contextual information by probing the incoming features with filters or pooling operations at multiple rates and multiple effective fields-of-view, while the latter networks can capture sharper object boundaries by gradually recovering the spatial information.