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LLVIP
LLVIP: A Visible-infrared Paired Dataset for Low-light VisionMeta-SelfLearning
Meta Self-learning for Multi-Source Domain Adaptation: A BenchmarkBCI
BCI: Breast Cancer Immunohistochemical Image Generation through Pyramid Pix2pixHHCL-ReID
Hard-sample Guided Hybrid Contrast Learning for Unsupervised Person Re-IdentificationCAC-UNet-DigestPath2019
1st to MICCAI DigestPath2019 challenge (https://digestpath2019.grand-challenge.org/Home/) on colonoscopy tissue segmentation and classification task. (MICCAI 2019) https://teacher.bupt.edu.cn/zhuchuang/en/index.htmIAST-ECCV2020
IAST: Instance Adaptive Self-training for Unsupervised Domain Adaptation (ECCV 2020) https://teacher.bupt.edu.cn/zhuchuang/en/index.htmBALNMP
Predicting Axillary Lymph Node Metastasis in Early Breast Cancer Using Deep Learning on Primary Tumor Biopsy Slides, BCNB DatasetPGDF
Sample Prior Guided Robust Model Learning to Suppress Noisy LabelsHSA-NRL
Hard Sample Aware Noise Robust Learning forHistopathology Image ClassificationThyroid-Cytopathological-Diagnosis-with-AMIL_MSFF
Attention Based Multi-Instance Thyroid Cytopathological Diagnosis with Multi-Scale Feature FusionTCVC
Code of paper "Temporal Consistent Automatic Video Colorization via Semantic Correspondence"Glomeruli-Instance-Segmentation
bupt-ai-cz
The introduction and news of CVSM Group.Label-Noise-Robust-Training
Noise Robust Learning with Hard Example Aware for Pathological Image classificationThyroid-Nodule-Ultrasound-Image-Classification
Thyroid Nodule Ultrasound Image Classification Through Hybrid Feature Cropping NetworkMPFN
Ischemic Stroke Lesion Segmentation Using Multi-Plane Information FusionProML
code for "Semi-supervised Domain Adaptation via Prototype-based Multi-level Learning"ANRN
IAST-CAC-UNet-LLCNN-BreastCancerCNN-ImageRetrieval_DF_CDVS-Highly_Efficient_Follicular_Segmentation
Codes and Data for CVSM Group: 1. IAST: Instance Adaptive Self-training for Unsupervised Domain Adaptation (ECCV 2020); 2.WUDA
WUDATCNL
SMAF
code for paper “A SELF-TRAINING FRAMEWORK BASED ON MULTI-SCALE ATTENTION FUSION FOR WEAKLY SUPERVISED SEMANTIC SEGMENTATION”Love Open Source and this site? Check out how you can help us