https://github.com/BiQiWHU/CMFormer
공식 코드Learning Content-enhanced Mask Transformer for Domain Generalized Urban-Scene Segmentation
Papers with Code 아카이브
Synthetic-to-Real Translation
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Synthetic-to-real translation is the task of domain adaptation from synthetic (or virtual) data to real data.
<span style="color:grey; opacity: 0.6">( Image credit: CYCADA )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/GaoLii/DSP
공식 코드DSP: Dual Soft-Paste for Unsupervised Domain Adaptive Semantic Segmentation
https://github.com/HsinYingLee/DRIT
공식 코드Diverse Image-to-Image Translation via Disentangled Representations
https://github.com/JDAI-CV/FADA
공식 코드Classes Matter: A Fine-grained Adversarial Approach to Cross-domain Semantic Segmentation
https://github.com/KaimingHe/resnet-1k-layers
공식 코드https://github.com/RogerZhangzz/CAG_UDA
공식 코드Category Anchor-Guided Unsupervised Domain Adaptation for Semantic Segmentation
https://github.com/RoyalVane/CLAN
공식 코드https://github.com/TingHLiao/ELDA
공식 코드ELDA: Using Edges to Have an Edge on Semantic Segmentation Based UDA
https://github.com/XingangPan/Switchable-Whitening
공식 코드https://github.com/YangZhang4065/AdaptationSeg
공식 코드Curriculum Domain Adaptation for Semantic Segmentation of Urban Scenes
https://github.com/a514514772/DISE-Domain-Invariant-Structure-Extraction
공식 코드https://github.com/alpc91/transda
공식 코드Smoothing Matters: Momentum Transformer for Domain Adaptive Semantic Segmentation
https://github.com/aroitberg/sims4action
공식 코드https://github.com/binhuixie/spcl
공식 코드https://github.com/bit-da/sepico
공식 코드SePiCo: Semantic-Guided Pixel Contrast for Domain Adaptive Semantic Segmentation
https://github.com/bupt-ai-cz/IAST-ECCV2020
공식 코드Instance Adaptive Self-Training for Unsupervised Domain Adaptation
https://github.com/bupt-ai-cz/hiast
공식 코드Hard-aware Instance Adaptive Self-training for Unsupervised Cross-domain Semantic Segmentation
https://github.com/chen742/DCF
공식 코드Transferring to Real-World Layouts: A Depth-aware Framework for Scene Adaptation
https://github.com/chen742/PiPa
공식 코드PiPa: Pixel- and Patch-wise Self-supervised Learning for Domain Adaptative Semantic Segmentation
https://github.com/cyang-cityu/MetaCorrection
공식 코드https://github.com/engrjavediqbal/MLSL
공식 코드https://github.com/feipan664/IntraDA
공식 코드Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-Supervision
https://github.com/hmrc-ael/tridentadapt
공식 코드https://github.com/huitangtang/DADA-AAAI2020
공식 코드https://github.com/jhoffman/cycada_release
공식 코드https://github.com/kaiizhang/mfa
공식 코드Multiple Fusion Adaptation: A Strong Framework for Unsupervised Semantic Segmentation Adaptation
https://github.com/kgl-prml/Pixel-Level-Cycle-Association
공식 코드Pixel-Level Cycle Association: A New Perspective for Domain Adaptive Semantic Segmentation
https://github.com/layumi/AdaBoost_Seg
공식 코드Adaptive Boosting for Domain Adaptation: Towards Robust Predictions in Scene Segmentation
https://github.com/layumi/Cifar10-Adaboost
공식 코드Adaptive Boosting for Domain Adaptation: Towards Robust Predictions in Scene Segmentation
https://github.com/layumi/Seg-Uncertainty
공식 코드Unsupervised Scene Adaptation with Memory Regularization in vivo
https://github.com/lhoyer/DAFormer
공식 코드https://github.com/lhoyer/hrda
공식 코드HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentation
https://github.com/lhoyer/mic
공식 코드MIC: Masked Image Consistency for Context-Enhanced Domain Adaptation
https://github.com/liyunsheng13/BDL
공식 코드Bidirectional Learning for Domain Adaptation of Semantic Segmentation
https://github.com/ljjcoder/ehtdi
공식 코드https://github.com/lslrh/cpsl
공식 코드Class-Balanced Pixel-Level Self-Labeling for Domain Adaptive Semantic Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.