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Semi-Supervised Semantic Segmentation

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보존된 평가 기록: 438

Models that are trained with a small number of labeled examples and a large number of unlabeled examples and whose aim is to learn to segment an image (i.e. assign a class to every pixel).

코드

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

https://github.com/s4mcontext/s4mc

공식 코드

Semi-Supervised Semantic Segmentation via Marginal Contextual Information

https://github.com/senyh/ucmt

공식 코드

Co-training with High-Confidence Pseudo Labels for Semi-supervised Medical Image Segmentation

https://github.com/sud0301/semisup-semseg

공식 코드

Semi-Supervised Semantic Segmentation with High- and Low-level Consistency

https://github.com/tarun005/USSS_ICCV19

공식 코드

Universal Semi-Supervised Semantic Segmentation

https://github.com/usr922/fst

공식 코드

Learning from Future: A Novel Self-Training Framework for Semantic Segmentation

https://github.com/voldemortX/DST-CBC

공식 코드

DMT: Dynamic Mutual Training for Semi-Supervised Learning

https://github.com/wangshanwen001/rs-muca

공식 코드

Semi-supervised Semantic Segmentation for Remote Sensing Images via Multi-scale Uncertainty Consistency and Cross-Teacher-Student Attention

https://github.com/wooseok-shin/PrevMatch

공식 코드

Revisiting and Maximizing Temporal Knowledge in Semi-supervised Semantic Segmentation

https://github.com/xiaoaoran/SynLiDAR

공식 코드

Transfer Learning from Synthetic to Real LiDAR Point Cloud for Semantic Segmentation

https://github.com/xiaoyao3302/CCVC

공식 코드

Conflict-Based Cross-View Consistency for Semi-Supervised Semantic Segmentation

https://github.com/xinge008/Cylinder3D

공식 코드

Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation

https://github.com/xmed-lab/AllSpark

공식 코드

AllSpark: Reborn Labeled Features from Unlabeled in Transformer for Semi-Supervised Semantic Segmentation

https://github.com/yaoqi-zd/SGAN

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Saliency Guided Self-attention Network for Weakly and Semi-supervised Semantic Segmentation

https://github.com/yassouali/CCT

공식 코드

Semi-Supervised Semantic Segmentation with Cross-Consistency Training

https://github.com/yh-pengtu/GuidedMix-Net

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GuidedMix-Net: Learning to Improve Pseudo Masks Using Labeled Images as Reference

https://github.com/yukekejan/uccl

공식 코드

Uncertainty-Participation Context Consistency Learning for Semi-supervised Semantic Segmentation

https://github.com/ywher/ss-ada

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SS-ADA: A Semi-Supervised Active Domain Adaptation Framework for Semantic Segmentation

https://github.com/yyliu01/ps-mt

공식 코드

Perturbed and Strict Mean Teachers for Semi-supervised Semantic Segmentation

https://github.com/yyliu01/tracoco

공식 코드

Translation Consistent Semi-supervised Segmentation for 3D Medical Images

https://github.com/zdk258/TokenSwap

공식 코드

Exploring Token-Level Augmentation in Vision Transformer for Semi-Supervised Semantic Segmentation

https://github.com/zhenzhao/augseg

공식 코드

Augmentation Matters: A Simple-yet-Effective Approach to Semi-supervised Semantic Segmentation

https://github.com/zhenzhao/imas

공식 코드

Instance-specific and Model-adaptive Supervision for Semi-supervised Semantic Segmentation

https://github.com/ziyangwang007/cv-ssl-mis

공식 코드

Triple-View Feature Learning for Medical Image Segmentation

https://github.com/CuberrChen/AdvSemiSeg-Paddle

Adversarial Learning for Semi-Supervised Semantic Segmentation

https://github.com/GoNgXiAoPeNg1/caffeBVLCplus

Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation

https://github.com/HiLab-git/DTC

Semi-Supervised Semantic Segmentation with Cross-Consistency Training

https://github.com/HyeonwooNoh/caffe

Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation

https://github.com/INK-USC/DualRE

Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results

https://github.com/KookHoiKim/AdaptSegNet

Adversarial Learning for Semi-Supervised Semantic Segmentation

https://github.com/L-Reichardt/Cylinder3D-updated-CUDA

Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation

https://github.com/Lan1991Xu/ONE_NeurIPS2018

Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results

https://github.com/LiheYoung/MiningFSS

Part-aware Prototype Network for Few-shot Semantic Segmentation

https://github.com/Luoxd1996/DTC

Semi-Supervised Semantic Segmentation with Cross-Consistency Training

https://github.com/MindSpore-scientific/code-13/tree/main/Translation-Invariant/model

Translation Consistent Semi-supervised Segmentation for 3D Medical Images

https://github.com/NiteshBharadwaj/adaptsegnet-materials

Adversarial Learning for Semi-Supervised Semantic Segmentation

https://github.com/Silence-Monk/Silence-Monk-Papers-Online

PseudoSeg: Designing Pseudo Labels for Semantic Segmentation

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