https://github.com/hengcai-nju/desco
공식 코드Orthogonal Annotation Benefits Barely-supervised Medical Image Segmentation
Papers with Code 아카이브
2D Semantic Segmentation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 17
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- 11
- 보존된 평가 기록
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/henghuiding/MOSE-api
공식 코드MOSE: A New Dataset for Video Object Segmentation in Complex Scenes
https://github.com/henghuiding/MeViS
공식 코드MeViS: A Large-scale Benchmark for Video Segmentation with Motion Expressions
https://github.com/hengliusky/few_shot_rvos
공식 코드Learning Cross-Modal Affinity for Referring Video Object Segmentation Targeting Limited Samples
https://github.com/henry991115/mcfnet
공식 코드Multi-scale Cascaded Large-Model for Whole-body ROI Segmentation
https://github.com/hermannsblum/scim
공식 코드SCIM: Simultaneous Clustering, Inference, and Mapping for Open-World Semantic Scene Understanding
https://github.com/herschel555/caml
공식 코드Correlation-Aware Mutual Learning for Semi-supervised Medical Image Segmentation
https://github.com/heshuting555/dshmp
공식 코드Decoupling Static and Hierarchical Motion Perception for Referring Video Segmentation
https://github.com/heshuting555/refmask3d
공식 코드RefMask3D: Language-Guided Transformer for 3D Referring Segmentation
https://github.com/hfslyc/GCPNet
공식 코드https://github.com/hfut-xc-yun/SpecTr
공식 코드SpecTr: Spectral Transformer for Hyperspectral Pathology Image Segmentation
https://github.com/hhankyangg/sam-unet
공식 코드SAM-UNet:Enhancing Zero-Shot Segmentation of SAM for Universal Medical Images
https://github.com/hhj1897/face_parsing
공식 코드RoI Tanh-polar Transformer Network for Face Parsing in the Wild
https://github.com/hhyhrhy/owmm-agent
공식 코드OWMM-Agent: Open World Mobile Manipulation With Multi-modal Agentic Data Synthesis
https://github.com/hic-messaoudi/cross-dimensional-transfer-learning-in-medical-image-segmentation-with-deep-learning
공식 코드Cross-dimensional transfer learning in medical image segmentation with deep learning
https://github.com/hidneuron/3d-effivitcaps
공식 코드3D-EffiViTCaps: 3D Efficient Vision Transformer with Capsule for Medical Image Segmentation
https://github.com/hiest/distmis
공식 코드Distributing Deep Learning Hyperparameter Tuning for 3D Medical Image Segmentation
https://github.com/hifexplo/masking
공식 코드https://github.com/hikopensource/davar-lab-ocr
공식 코드SPIN: Structure-Preserving Inner Offset Network for Scene Text Recognition
https://github.com/hila-chefer/Transformer-MM-Explainability
공식 코드Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers
https://github.com/hilab-git/cdma
공식 코드Semi-supervised Pathological Image Segmentation via Cross Distillation of Multiple Attentions
https://github.com/hilab-git/dag4mia
공식 코드https://github.com/hilab-git/dca-net
공식 코드Domain Composition and Attention for Unseen-Domain Generalizable Medical Image Segmentation
https://github.com/hilab-git/fpl-plus
공식 코드https://github.com/hilab-git/pa-seg
공식 코드https://github.com/hilab-git/pymic
공식 코드PyMIC: A deep learning toolkit for annotation-efficient medical image segmentation
https://github.com/hilab-git/pymic_examples
공식 코드PyMIC: A deep learning toolkit for annotation-efficient medical image segmentation
https://github.com/hilab-git/segrap2023
공식 코드https://github.com/hilab-git/srpl-sfda
공식 코드https://github.com/hilab-git/ugtst
공식 코드https://github.com/hilab-git/upl-sfda
공식 코드https://github.com/hilab-git/word
공식 코드https://github.com/hiltonml/camera_trap_tools
공식 코드An Image Processing Pipeline for Camera Trap Time-Lapse Recordings
https://github.com/himashi92/Duo-SegNet
공식 코드Duo-SegNet: Adversarial Dual-Views for Semi-Supervised Medical Image Segmentation
https://github.com/hit-sirs/crobim
공식 코드Cross-Modal Bidirectional Interaction Model for Referring Remote Sensing Image Segmentation
https://github.com/hitachinsk/samed
공식 코드Customized Segment Anything Model for Medical Image Segmentation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.