Papers with Code 아카이브

Semantic Segmentation

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

29

평가표

150

보존된 평가 기록

1,918

코드

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

https://github.com/cucl-2/dc-seg

공식 코드

DC-Seg: Disentangled Contrastive Learning for Brain Tumor Segmentation with Missing Modalities

https://github.com/cuhk-aim-group/diffrect

공식 코드

DiffRect: Latent Diffusion Label Rectification for Semi-supervised Medical Image Segmentation

https://github.com/cuhk-aim-group/un-sam

공식 코드

UN-SAM: Universal Prompt-Free Segmentation for Generalized Nuclei Images

https://github.com/cuiziteng/illumination-adaptive-transformer

공식 코드

You Only Need 90K Parameters to Adapt Light: A Light Weight Transformer for Image Enhancement and Exposure Correction

https://github.com/curli-quan/slide-sam

공식 코드

Slide-SAM: Medical SAM Meets Sliding Window

https://github.com/cuthbert-huang/cc-net

공식 코드

Complementary consistency semi-supervised learning for 3D left atrial image segmentation

https://github.com/cuthbert-huang/cpcanet

공식 코드

Channel prior convolutional attention for medical image segmentation

https://github.com/cuzaoo/sanet-main

공식 코드

Spatial-Assistant Encoder-Decoder Network for Real Time Semantic Segmentation

https://github.com/cuzyoung/crossearth

공식 코드

CrossEarth: Geospatial Vision Foundation Model for Domain Generalizable Remote Sensing Semantic Segmentation

https://github.com/cv-rits/coarse3d

공식 코드

COARSE3D: Class-Prototypes for Contrastive Learning in Weakly-Supervised 3D Point Cloud Segmentation

https://github.com/cv-rits/densemtl

공식 코드

DenseMTL: Cross-task Attention Mechanism for Dense Multi-task Learning

https://github.com/cv-shuchanglyu/sam-joanet

공식 코드

Joint-Optimized Unsupervised Adversarial Domain Adaptation in Remote Sensing Segmentation with Prompted Foundation Model

https://github.com/cv516buaa/mmotu_ds2net

공식 코드

MMOTU: A Multi-Modality Ovarian Tumor Ultrasound Image Dataset for Unsupervised Cross-Domain Semantic Segmentation

https://github.com/cv516buaa/sd-aanet

공식 코드

A Self-Distillation Embedded Supervised Affinity Attention Model for Few-Shot Segmentation

https://github.com/cv516buaa/st-dasegnet

공식 코드

Self-Training Guided Disentangled Adaptation for Cross-Domain Remote Sensing Image Semantic Segmentation

https://github.com/cvai-repo/duality-diagram-similarity

공식 코드

Duality Diagram Similarity: a generic framework for initialization selection in task transfer learning

https://github.com/cvblab/prostate_wsss_weglenet

공식 코드

WeGleNet: A Weakly-Supervised Convolutional Neural Network for the Semantic Segmentation of Gleason Grades in Prostate Histology Images

https://github.com/cveo/s3net

공식 코드

S3Net: Innovating Stereo Matching and Semantic Segmentation with a Single-Branch Semantic Stereo Network in Satellite Epipolar Imagery

https://github.com/cvg/labelmaker

공식 코드

LABELMAKER: Automatic Semantic Label Generation from RGB-D Trajectories

https://github.com/cvi-szu/ccam

공식 코드

Contrastive learning of Class-agnostic Activation Map for Weakly Supervised Object Localization and Semantic Segmentation

https://github.com/cvi-szu/clims

공식 코드

Cross Language Image Matching for Weakly Supervised Semantic Segmentation

https://github.com/cvi-szu/qa-clims

공식 코드

Question-Answer Cross Language Image Matching for Weakly Supervised Semantic Segmentation

https://github.com/cvlab-columbia/equi4rob

공식 코드

Robust Perception through Equivariance

https://github.com/cvlab-epfl/detecting-the-unexpected

공식 코드

Detecting the Unexpected via Image Resynthesis

https://github.com/cvlab-kaist/SOLA

공식 코드

Referring Video Object Segmentation via Language-aligned Track Selection

https://github.com/cvlab-stonybrook/SelfMedMAE

공식 코드

Self Pre-training with Masked Autoencoders for Medical Image Classification and Segmentation

https://github.com/cvlab-stonybrook/Weighting-Pseudo-Labels

공식 코드

Weighting Pseudo-Labels via High-Activation Feature Index Similarity and Object Detection for Semi-Supervised Segmentation

https://github.com/cvlab-stonybrook/beyond-pixels

공식 코드

Beyond Pixels: Semi-Supervised Semantic Segmentation with a Multi-scale Patch-based Multi-Label Classifier

https://github.com/cvlab-stonybrook/plm_ssl

공식 코드

Precise Location Matching Improves Dense Contrastive Learning in Digital Pathology

https://github.com/cvlab-stonybrook/tokensparse-for-medseg

공식 코드

Token Sparsification for Faster Medical Image Segmentation

https://github.com/cvlab-unibo/d4-dbst

공식 코드

Plugging Self-Supervised Monocular Depth into Unsupervised Domain Adaptation for Semantic Segmentation

https://github.com/cvlab-unibo/mm2d3d

공식 코드

Exploiting the Complementarity of 2D and 3D Networks to Address Domain-Shift in 3D Semantic Segmentation

https://github.com/cvlab-unibo/shallow_da

공식 코드

Shallow Features Guide Unsupervised Domain Adaptation for Semantic Segmentation at Class Boundaries

https://github.com/cvmi-lab/doda

공식 코드

DODA: Data-oriented Sim-to-Real Domain Adaptation for 3D Semantic Segmentation

https://github.com/cvmi-lab/mars3d

공식 코드

MarS3D: A Plug-and-Play Motion-Aware Model for Semantic Segmentation on Multi-Scan 3D Point Clouds

https://github.com/cvmi-lab/pla

공식 코드

PLA: Language-Driven Open-Vocabulary 3D Scene Understanding

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