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How to Efficiently Annotate Images for Best-Performing Deep Learning Based Segmentation Models: An Empirical Study with Weak and Noisy Annotations and Segment Anything Model

How to Efficiently Annotate Images for Best-Performing Deep Learning Based Segmentation Models: An Empirical Study with Weak and Noisy Annotations and Segment Anything Model

17 December 2023
Yixin Zhang
Shen Zhao
Han Gu
Maciej Mazurowski
    VLM
ArXivPDFHTML

Papers citing "How to Efficiently Annotate Images for Best-Performing Deep Learning Based Segmentation Models: An Empirical Study with Weak and Noisy Annotations and Segment Anything Model"

4 / 4 papers shown
Title
Semi-Supervised Object Detection: A Survey on Progress from CNN to
  Transformer
Semi-Supervised Object Detection: A Survey on Progress from CNN to Transformer
Tahira Shehzadi
Ifza
Didier Stricker
Muhammad Zeshan Afzal
ViT
38
0
0
11 Jul 2024
Beyond Pixel-Wise Supervision for Medical Image Segmentation: From
  Traditional Models to Foundation Models
Beyond Pixel-Wise Supervision for Medical Image Segmentation: From Traditional Models to Foundation Models
Yuyan Shi
Jialu Ma
Jin Yang
Shasha Wang
Yichi Zhang
MedIm
VLM
19
2
0
20 Apr 2024
Masked Autoencoders Are Scalable Vision Learners
Masked Autoencoders Are Scalable Vision Learners
Kaiming He
Xinlei Chen
Saining Xie
Yanghao Li
Piotr Dollár
Ross B. Girshick
ViT
TPM
305
7,434
0
11 Nov 2021
Semantic Understanding of Scenes through the ADE20K Dataset
Semantic Understanding of Scenes through the ADE20K Dataset
Bolei Zhou
Hang Zhao
Xavier Puig
Tete Xiao
Sanja Fidler
Adela Barriuso
Antonio Torralba
SSeg
253
1,827
0
18 Aug 2016
1