Semantic Segmentation

5147 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 )


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Latest papers with no code

Improving Shift Invariance in Convolutional Neural Networks with Translation Invariant Polyphase Sampling

no code yet • 11 Apr 2024

Downsampling operators break the shift invariance of convolutional neural networks (CNNs) and this affects the robustness of features learned by CNNs when dealing with even small pixel-level shift.

Streamlined Photoacoustic Image Processing with Foundation Models: A Training-Free Solution

no code yet • 11 Apr 2024

Foundation models have rapidly evolved and have achieved significant accomplishments in computer vision tasks.

Exploiting Object-based and Segmentation-based Semantic Features for Deep Learning-based Indoor Scene Classification

no code yet • 11 Apr 2024

Hence, a novel approach that uses a semantic segmentation mask to provide Hu-moments-based segmentation categories' shape characterization, designated by Segmentation-based Hu-Moments Features (SHMFs), is proposed.

GLID: Pre-training a Generalist Encoder-Decoder Vision Model

no code yet • 11 Apr 2024

This paper proposes a GeneraLIst encoder-Decoder (GLID) pre-training method for better handling various downstream computer vision tasks.

Implicit and Explicit Language Guidance for Diffusion-based Visual Perception

no code yet • 11 Apr 2024

The explicit branch utilizes the ground-truth labels of corresponding images as text prompts to condition feature extraction of diffusion model.

Multi-rater Prompting for Ambiguous Medical Image Segmentation

no code yet • 11 Apr 2024

In this paper, we tackle two challenges arisen in multi-rater annotations for medical image segmentation (called ambiguous medical image segmentation): (1) How to train a deep learning model when a group of raters produces a set of diverse but plausible annotations, and (2) how to fine-tune the model efficiently when computation resources are not available for re-training the entire model on a different dataset domain.

LUCF-Net: Lightweight U-shaped Cascade Fusion Network for Medical Image Segmentation

no code yet • 11 Apr 2024

In this study, the performance of existing U-shaped neural network architectures was enhanced for medical image segmentation by adding Transformer.

Convolution-based Probability Gradient Loss for Semantic Segmentation

no code yet • 10 Apr 2024

In this paper, we introduce a novel Convolution-based Probability Gradient (CPG) loss for semantic segmentation.

An Evidential-enhanced Tri-Branch Consistency Learning Method for Semi-supervised Medical Image Segmentation

no code yet • 10 Apr 2024

Additionally, the evidential fusion branch capitalizes on the complementary attributes of the first two branches and leverages an evidence-based Dempster-Shafer fusion strategy, supervised by more reliable and accurate pseudo-labels of unlabeled data.

RESSCAL3D: Resolution Scalable 3D Semantic Segmentation of Point Clouds

no code yet • 10 Apr 2024

To the best of our knowledge, the proposed method is the first to propose a resolution-scalable approach for 3D semantic segmentation of point clouds based on deep learning.