https://github.com/mdciri/augmentation
공식 코드What is the best data augmentation for 3D brain tumor segmentation?
Papers with Code 아카이브
Semantic Segmentation
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/meatery/semantic-segmentation
공식 코드Deep Image Semantic Communication Model for Artificial Intelligent Internet of Things
https://github.com/meclabtuda/atlas-replay
공식 코드https://github.com/meclabtuda/med-nca
공식 코드Med-NCA: Robust and Lightweight Segmentation with Neural Cellular Automata
https://github.com/med-air/3dsam-adapter
공식 코드3DSAM-adapter: Holistic adaptation of SAM from 2D to 3D for promptable tumor segmentation
https://github.com/med-air/cmc
공식 코드Robust Semi-supervised Multimodal Medical Image Segmentation via Cross Modality Collaboration
https://github.com/med-air/fgrm
공식 코드Uncertainty Estimation for Safety-critical Scene Segmentation via Fine-grained Reward Maximization
https://github.com/med-air/gazemedseg
공식 코드Weakly-supervised Medical Image Segmentation with Gaze Annotations
https://github.com/med-air/iop-fl
공식 코드IOP-FL: Inside-Outside Personalization for Federated Medical Image Segmentation
https://github.com/medaitech/rab
공식 코드https://github.com/medaitech/tcmn
공식 코드One-Shot Medical Video Object Segmentation via Temporal Contrastive Memory Networks
https://github.com/medcai/l2snet
공식 코드https://github.com/meddjilani/fashionrecommender
공식 코드Trend-Aware Fashion Recommendation with Visual Segmentation and Semantic Similarity
https://github.com/meder411/Tangent-Images
공식 코드https://github.com/medicinetoken/medical-sam2
공식 코드Medical SAM 2: Segment medical images as video via Segment Anything Model 2
https://github.com/medicl-vu/cats
공식 코드https://github.com/medicl-vu/colossal
공식 코드COLosSAL: A Benchmark for Cold-start Active Learning for 3D Medical Image Segmentation
https://github.com/medicl-vu/prism
공식 코드PRISM: A Promptable and Robust Interactive Segmentation Model with Visual Prompts
https://github.com/medicl-vu/promise
공식 코드Promise:Prompt-driven 3D Medical Image Segmentation Using Pretrained Image Foundation Models
https://github.com/medicl-vu/variability
공식 코드https://github.com/medimair/brain-mri-processing-pipeline
공식 코드Negligible effect of brain MRI data preprocessing for tumor segmentation
https://github.com/megvii-model/FunnelAct
공식 코드https://github.com/megvii-research/LGD
공식 코드https://github.com/megvii-research/SOLQ
공식 코드https://github.com/megvii-research/TLC
공식 코드Improving Image Restoration by Revisiting Global Information Aggregation
https://github.com/megvii-research/pcb
공식 코드Relieving Long-tailed Instance Segmentation via Pairwise Class Balance
https://github.com/megvii-research/petr
공식 코드PETRv2: A Unified Framework for 3D Perception from Multi-Camera Images
https://github.com/megvii-research/revcol
공식 코드https://github.com/megvii-research/treeenergyloss
공식 코드Tree Energy Loss: Towards Sparsely Annotated Semantic Segmentation
https://github.com/megvii-research/us3l-cvpr2023
공식 코드Three Guidelines You Should Know for Universally Slimmable Self-Supervised Learning
https://github.com/meidachen/STPLS3D
공식 코드STPLS3D: A Large-Scale Synthetic and Real Aerial Photogrammetry 3D Point Cloud Dataset
https://github.com/meldproject/meld_graph
공식 코드https://github.com/melinphd/taal
공식 코드TAAL: Test-time Augmentation for Active Learning in Medical Image Segmentation
https://github.com/mellody11/less
공식 코드LESS: Label-Efficient and Single-Stage Referring 3D Segmentation
https://github.com/melqkiades/deep-wetlands
공식 코드https://github.com/mendelxu/san
공식 코드Side Adapter Network for Open-Vocabulary Semantic Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.