Papers with Code 아카이브
2D Semantic Segmentation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 17
- 평가표
- 11
- 보존된 평가 기록
- 42
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/kamiLight/CADepth-master
공식 코드Channel-Wise Attention-Based Network for Self-Supervised Monocular Depth Estimation
https://github.com/kanezaki/pytorch-unsupervised-segmentation-tip
공식 코드Unsupervised Learning of Image Segmentation Based on Differentiable Feature Clustering
https://github.com/kanghoonyoon/hetsgg-torch
공식 코드Unbiased Heterogeneous Scene Graph Generation with Relation-aware Message Passing Neural Network
https://github.com/kanji95/SHNET
공식 코드Comprehensive Multi-Modal Interactions for Referring Image Segmentation
https://github.com/kanji95/Talk2car-Refseg
공식 코드https://github.com/kartik-3004/segface
공식 코드https://github.com/kashyap7x/QGN
공식 코드Quadtree Generating Networks: Efficient Hierarchical Scene Parsing with Sparse Convolutions
https://github.com/kasiv008/OFFSEG
공식 코드OFFSEG: A Semantic Segmentation Framework For Off-Road Driving
https://github.com/kathyliu579/contrastivecross-teachingwithregistration
공식 코드Learning Semi-Supervised Medical Image Segmentation from Spatial Registration
https://github.com/kathyliu579/cs-unet
공식 코드Optimizing Vision Transformers for Medical Image Segmentation
https://github.com/kav-institute/imptc-dataset
공식 코드The IMPTC Dataset: An Infrastructural Multi-Person Trajectory and Context Dataset
https://github.com/kbui1993/L1mL2Segmentation
공식 코드https://github.com/kbui1993/matlab_potts
공식 코드Difference of Anisotropic and Isotropic TV for Segmentation under Blur and Poisson Noise
https://github.com/kbui1993/official_aitv_sat_slat
공식 코드https://github.com/kclip/localized-adaptive-risk-control
공식 코드https://github.com/kdwonn/SaG
공식 코드Shatter and Gather: Learning Referring Image Segmentation with Text Supervision
https://github.com/keeganhk/pointvst
공식 코드https://github.com/keeplearning-again/matchseg
공식 코드MatchSeg: Towards Better Segmentation via Reference Image Matching
https://github.com/keillernogueira/data-centric-rs-classification
공식 코드Core-Set Selection for Data-efficient Land Cover Segmentation
https://github.com/kemaloksuz/LRP-Error
공식 코드https://github.com/kent0n-li/medical-image-segmentation
공식 코드nnSAM: Plug-and-play Segment Anything Model Improves nnUNet Performance
https://github.com/kepengxu/pgtformer
공식 코드Beyond Alignment: Blind Video Face Restoration via Parsing-Guided Temporal-Coherent Transformer
https://github.com/kerem-cekmeceli/foundation-models-for-medical-imagery
공식 코드Do Vision Foundation Models Enhance Domain Generalization in Medical Image Segmentation?
https://github.com/kevinlin311tw/CDCL-human-part-segmentation
공식 코드Cross-Domain Complementary Learning Using Pose for Multi-Person Part Segmentation
https://github.com/kfan21/eoras
공식 코드https://github.com/kibrom2b/LFB-Net
공식 코드Learning With Context Feedback Loop for Robust Medical Image Segmentation
https://github.com/kidswithtokens/medical-sam-adapter
공식 코드Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation
https://github.com/kidswithtokens/medsegdiff
공식 코드MedSegDiff-V2: Diffusion based Medical Image Segmentation with Transformer
https://github.com/kidswithtokens/one-prompt
공식 코드https://github.com/kiharalab/acc-unet
공식 코드ACC-UNet: A Completely Convolutional UNet model for the 2020s
https://github.com/kilianzepf/laplacian_segmentation
공식 코드Laplacian Segmentation Networks Improve Epistemic Uncertainty Quantification
https://github.com/kimjisoo12/guidednet
공식 코드GuidedNet: Semi-Supervised Multi-Organ Segmentation via Labeled Data Guide Unlabeled Data
https://github.com/kimsekeun/medivista-sam
공식 코드MediViSTA: Medical Video Segmentation via Temporal Fusion SAM Adaptation for Echocardiography
https://github.com/kimsunkyung/SCA_MTL
공식 코드https://github.com/kimtae55/DeepFDR
공식 코드DeepFDR: A Deep Learning-based False Discovery Rate Control Method for Neuroimaging Data
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.