Attention De-sparsification Matters: Inducing Diversity in Digital Pathology Representation Learning

12 September 2023

Papers citing "Attention De-sparsification Matters: Inducing Diversity in Digital Pathology Representation Learning"

6 / 6 papers shown

Title
What Do Self-Supervised Vision Transformers Learn? Namuk Park Wonjae Kim Byeongho Heo Taekyung Kim Sangdoo Yun SSL 67 76 1 01 May 2023
Self-Supervised Visual Representation Learning with Semantic Grouping Xin Wen Bingchen Zhao Anlin Zheng Xinming Zhang Xiaojuan Qi SSL 117 71 0 30 May 2022
GroupViT: Semantic Segmentation Emerges from Text Supervision Jiarui Xu Shalini De Mello Sifei Liu Wonmin Byeon Thomas Breuel Jan Kautz Xinyu Wang ViT VLM 189 499 0 22 Feb 2022
Masked Autoencoders Are Scalable Vision Learners Kaiming He Xinlei Chen Saining Xie Yanghao Li Piotr Dollár Ross B. Girshick ViT TPM 305 7,443 0 11 Nov 2021
BRACS: A Dataset for BReAst Carcinoma Subtyping in H&E Histology Images N. Brancati A. Anniciello Pushpak Pati D. Riccio G. Scognamiglio ... A. Foncubierta G. Botti M. Gabrani Florinda Feroce Maria Frucci 45 122 0 08 Nov 2021
Emerging Properties in Self-Supervised Vision Transformers Mathilde Caron Hugo Touvron Ishan Misra Hervé Jégou Julien Mairal Piotr Bojanowski Armand Joulin 317 5,785 0 29 Apr 2021