Papers with Code 아카이브

Image Segmentation

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 1
평가표: 13
보존된 평가 기록: 34

Image Segmentation is a computer vision task that involves dividing an image into multiple segments or regions, each of which corresponds to a different object or part of an object. The goal of image segmentation is to assign a unique label or category to each pixel in the image, so that pixels with similar attributes are grouped together.

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/CPJKU/audioLIME

공식 코드

Towards Musically Meaningful Explanations Using Source Separation

https://github.com/CSCYQJ/LOCATION-SENSITIVE-LOCAL-PROTOTYPE-NETWORK

공식 코드

A Location-Sensitive Local Prototype Network for Few-Shot Medical Image Segmentation

https://github.com/CUMC-Yuan-Lab/GCML/tree/main/SANet/BraTS

공식 코드

Automatic Brain Tumor Segmentation with Scale Attention Network

https://github.com/CV-IP/uqvi

공식 코드

Uncertainty Quantification using Variational Inference for Biomedical Image Segmentation

https://github.com/CVRL/OpenSourceIrisPAD

공식 코드

Open Source Presentation Attack Detection Baseline for Iris Recognition

https://github.com/Championchess/Amodal-Completion-in-the-Wild

공식 코드

Amodal Ground Truth and Completion in the Wild

https://github.com/ChenLiu-1996/CUTS

공식 코드

CUTS: A Deep Learning and Topological Framework for Multigranular Unsupervised Medical Image Segmentation

https://github.com/Chenglin-Yang/MOS_Meticulous-Object-Segmentation

공식 코드

Meticulous Object Segmentation

https://github.com/ChenyunWu/PhraseCutDataset

공식 코드

PhraseCut: Language-based Image Segmentation in the Wild

https://github.com/Chuanhai/Mask-Editor

공식 코드

Mask Editor : an Image Annotation Tool for Image Segmentation Tasks

https://github.com/ChunaraLab/FairDCL-mitigating-urban-rural-disparity

공식 코드

Mitigating Urban-Rural Disparities in Contrastive Representation Learning with Satellite Imagery

https://github.com/CompVis/mask

공식 코드

[MASK] is All You Need

https://github.com/DIAGNijmegen/neural-odes-segmentation

공식 코드

Neural Ordinary Differential Equations for Semantic Segmentation of Individual Colon Glands

https://github.com/DIAL-RPI/COVID19-ICUPrediction

공식 코드

Integrative Analysis for COVID-19 Patient Outcome Prediction

https://github.com/DIAL-RPI/PIPO-FAN

공식 코드

Multi-organ Segmentation over Partially Labeled Datasets with Multi-scale Feature Abstraction

https://github.com/DIVA-DIA/LayoutAnalysisEvaluator

공식 코드

Historical Document Image Segmentation with LDA-Initialized Deep Neural Networks

https://github.com/DLTK/DLTK

공식 코드

DLTK: State of the Art Reference Implementations for Deep Learning on Medical Images

https://github.com/DLTK/models

공식 코드

NeuroNet: Fast and Robust Reproduction of Multiple Brain Image Segmentation Pipelines

https://github.com/Danial-Qashqai/CSFNet

공식 코드

CSFNet: A Cosine Similarity Fusion Network for Real-Time RGB-X Semantic Segmentation of Driving Scenes

https://github.com/Daniyanaj/vMixer

공식 코드

Improving 3D Medical Image Segmentation at Boundary Regions using Local Self-attention and Global Volume Mixing

https://github.com/DarthReca/magnifier-california

공식 코드

Magnifier: A Multi-grained Neural Network-based Architecture for Burned Area Delineation

https://github.com/DebeshJha/2020-CBMS-DoubleU-Net

공식 코드

DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation

https://github.com/DebeshJha/ResUNetplusplus

공식 코드

ResUNet++: An Advanced Architecture for Medical Image Segmentation

https://github.com/DerrickWang005/CRIS.pytorch

공식 코드

CRIS: CLIP-Driven Referring Image Segmentation

https://github.com/Digital-Dermatology/t-loss

공식 코드

Robust T-Loss for Medical Image Segmentation

https://github.com/DinhHieuHoang/SAM3D

공식 코드

SAM3D: Segment Anything Model in Volumetric Medical Images

https://github.com/Dmmm1997/DeRIS

공식 코드

DeRIS: Decoupling Perception and Cognition for Enhanced Referring Image Segmentation through Loopback Synergy

https://github.com/EagleMIT/EMKD

공식 코드

Efficient Medical Image Segmentation Based on Knowledge Distillation

https://github.com/EmbodiedVision/slot_attention_normalization

공식 코드

Attention Normalization Impacts Cardinality Generalization in Slot Attention

https://github.com/EmmaW8/BEAL

공식 코드

Boundary and Entropy-driven Adversarial Learning for Fundus Image Segmentation

https://github.com/FGNN-Author/FGNN

공식 코드

Finite Group Equivariant Neural Networks for Games

https://github.com/FengheTan9/Medical-Image-Segmentation-Benchmarks

공식 코드

CMUNeXt: An Efficient Medical Image Segmentation Network based on Large Kernel and Skip Fusion

https://github.com/FengheTan9/Multi-Level-Global-Context-Cross-Consistency

공식 코드

Multi-Level Global Context Cross Consistency Model for Semi-Supervised Ultrasound Image Segmentation with Diffusion Model

https://github.com/FightingFighting/GPS

공식 코드

Gradient-based Parameter Selection for Efficient Fine-Tuning

https://github.com/ForawardStar/HIPe

공식 코드

Hierarchical Image Peeling: A Flexible Scale-space Filtering Framework

https://github.com/GinZhu/RDST

공식 코드

A residual dense vision transformer for medical image super-resolution with segmentation-based perceptual loss fine-tuning

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.