Semantic Segmentation 초기 FCN부터 Transformer Architecture를 사용한 최신 TransUnet까지 정보를 찾을 수 있는 링크들을 정리한 글입니다.
FCN paper review : medium.com/@msmapark2/fcn-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0-fully-convolutional-networks-for-semantic-segmentation-81f016d76204
FCN 논문 리뷰 — Fully Convolutional Networks for Semantic Segmentation
딥러닝 기반 OCR 스터디 — FCN 논문 리뷰
medium.com
U-Net paper review : medium.com/@msmapark2/u-net-%EB%85%BC%EB%AC%B8-%EB%A6%AC%EB%B7%B0-u-net-convolutional-networks-for-biomedical-image-segmentation-456d6901b28a
U-Net 논문 리뷰 — U-Net: Convolutional Networks for Biomedical Image Segmentation
딥러닝 기반 OCR 스터디 — U-Net 논문 리뷰
medium.com
U-Net paper : arxiv.org/abs/1505.04597
U-Net: Convolutional Networks for Biomedical Image Segmentation
There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated
arxiv.org
Deep-Lab V3+ paper review : medium.com/hyunjulie/2%ED%8E%B8-%EB%91%90-%EC%A0%91%EA%B7%BC%EC%9D%98-%EC%A0%91%EC%A0%90-deeplab-v3-ef7316d4209d
2편: 두 접근의 접점, DeepLab V3+
1편에서 소개했던 두가지 방법을 합쳐놓은, 두 세계가 만난 순간… 너의 가능성은?
medium.com
Transformer tensorflow implementation : wikidocs.net/31379
위키독스
온라인 책을 제작 공유하는 플랫폼 서비스
wikidocs.net
TransUnet paper : arxiv.org/abs/2102.04306
TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation
Medical image segmentation is an essential prerequisite for developing healthcare systems, especially for disease diagnosis and treatment planning. On various medical image segmentation tasks, the u-shaped architecture, also known as U-Net, has become the
arxiv.org
Vision Transformer paper review : engineer-mole.tistory.com/133
[논문] 최근 AI의 이미지 인식에서 화제인 "Vision Transformer"에 대한 해설
※ 일본 블로그 내용을 번역한 것으로 오역이나 직역이 있을 수 있으며, 내용의 오류 지적해주시면 감사하겠습니다. 1. 개요 현재 AI계에서 화제가 되고 있는 "Vision Transformer"에 대해 다뤄보려고
engineer-mole.tistory.com
Vision Transformer paper : arxiv.org/abs/2010.11929
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
While the Transformer architecture has become the de-facto standard for natural language processing tasks, its applications to computer vision remain limited. In vision, attention is either applied in conjunction with convolutional networks, or used to rep
arxiv.org
Vision Transformer pytorch implementation using einops : github.com/FrancescoSaverioZuppichini/ViT
FrancescoSaverioZuppichini/ViT
Implementing Vi(sion)T(transformer). Contribute to FrancescoSaverioZuppichini/ViT development by creating an account on GitHub.
github.com
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