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Semantic Segmentation
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/hrlblab/hats
공식 코드HATs: Hierarchical Adaptive Taxonomy Segmentation for Panoramic Pathology Image Analysis
https://github.com/hrlblab/layer_segmentation
공식 코드Cross-Species Data Integration for Enhanced Layer Segmentation in Kidney Pathology
https://github.com/hrlblab/molecularel
공식 코드Democratizing Pathological Image Segmentation with Lay Annotators via Molecular-empowered Learning
https://github.com/hrlblab/spatial_pathomics
공식 코드https://github.com/hrzhang1123/cvm_ws_segmentation
공식 코드https://github.com/hs-yn/daps
공식 코드Dense 2D-3D Indoor Prediction with Sound via Aligned Cross-Modal Distillation
https://github.com/hsiangyuzhao/adler
공식 코드AdLER: Adversarial Training with Label Error Rectification for One-Shot Medical Image Segmentation
https://github.com/hsiangyuzhao/rcps
공식 코드RCPS: Rectified Contrastive Pseudo Supervision for Semi-Supervised Medical Image Segmentation
https://github.com/hsp-iit/fast-ycb
공식 코드ROFT: Real-Time Optical Flow-Aided 6D Object Pose and Velocity Tracking
https://github.com/hsp-iit/online-detection
공식 코드Learn Fast, Segment Well: Fast Object Segmentation Learning on the iCub Robot
https://github.com/hsp-iit/roft
공식 코드ROFT: Real-Time Optical Flow-Aided 6D Object Pose and Velocity Tracking
https://github.com/hszhao/ICNet
공식 코드ICNet for Real-Time Semantic Segmentation on High-Resolution Images
https://github.com/hszhao/PSPNet
공식 코드https://github.com/htcr/sam_road
공식 코드https://github.com/htqin/awesome-model-quantization
공식 코드https://github.com/hu64/centerpoly
공식 코드CenterPoly: real-time instance segmentation using bounding polygons
https://github.com/huaaaliu/rgbx_semantic_segmentation
공식 코드CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers
https://github.com/huaibei-normal-university-cv-laboratory/mbdresunet
공식 코드MBDRes-U-Net: Multi-Scale Lightweight Brain Tumor Segmentation Network
https://github.com/huangdlab/enformer
공식 코드https://github.com/huanglizi/lvit
공식 코드LViT: Language meets Vision Transformer in Medical Image Segmentation
https://github.com/huanglizi/scribblevc
공식 코드ScribbleVC: Scribble-supervised Medical Image Segmentation with Vision-Class Embedding
https://github.com/huanglizi/stpnet
공식 코드STPNet: Scale-aware Text Prompt Network for Medical Image Segmentation
https://github.com/huanglizi/tfcns
공식 코드TFCNs: A CNN-Transformer Hybrid Network for Medical Image Segmentation
https://github.com/huangmozhilv/u2net_torch
공식 코드3D U$^2$-Net: A 3D Universal U-Net for Multi-Domain Medical Image Segmentation
https://github.com/huangshiqi128/zori
공식 코드ZoRI: Towards Discriminative Zero-Shot Remote Sensing Instance Segmentation
https://github.com/huangyjhust/3D-RU-Net
공식 코드3D RoI-aware U-Net for Accurate and Efficient Colorectal Tumor Segmentation
https://github.com/huangzizheng01/shufflemamba
공식 코드Stochastic Layer-Wise Shuffle: A Good Practice to Improve Vision Mamba Training
https://github.com/huawei-noah/CV-backbones
공식 코드https://github.com/huawei-noah/noah-research/tree/master/TokenFusion
공식 코드https://github.com/huggingface/pytorch-image-models
공식 코드https://github.com/hugo-oliveira/CoDAGANs
공식 코드Truly Generalizable Radiograph Segmentation with Conditional Domain Adaptation
https://github.com/hugo-oliveira/fsws_metalearning
공식 코드Meta-Learners for Few-Shot Weakly-Supervised Medical Image Segmentation
https://github.com/hugo-oliveira/openseg
공식 코드https://github.com/hugotessier-lab/neural-network-shrinking
공식 코드Energy Consumption Analysis of pruned Semantic Segmentation Networks on an Embedded GPU
https://github.com/hui-po-wang/progfed
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.