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ViC-MAE: Self-Supervised Representation Learning from Images and Video
  with Contrastive Masked Autoencoders

ViC-MAE: Self-Supervised Representation Learning from Images and Video with Contrastive Masked Autoencoders

21 March 2023
J. Hernandez
Ruben Villegas
Vicente Ordonez
    SSL
ArXivPDFHTML

Papers citing "ViC-MAE: Self-Supervised Representation Learning from Images and Video with Contrastive Masked Autoencoders"

5 / 5 papers shown
Title
Omnivore: A Single Model for Many Visual Modalities
Omnivore: A Single Model for Many Visual Modalities
Rohit Girdhar
Mannat Singh
Nikhil Ravi
L. V. D. van der Maaten
Armand Joulin
Ishan Misra
209
222
0
20 Jan 2022
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
258
7,337
0
11 Nov 2021
Emerging Properties in Self-Supervised Vision Transformers
Emerging Properties in Self-Supervised Vision Transformers
Mathilde Caron
Hugo Touvron
Ishan Misra
Hervé Jégou
Julien Mairal
Piotr Bojanowski
Armand Joulin
283
5,723
0
29 Apr 2021
Is Space-Time Attention All You Need for Video Understanding?
Is Space-Time Attention All You Need for Video Understanding?
Gedas Bertasius
Heng Wang
Lorenzo Torresani
ViT
278
1,939
0
09 Feb 2021
Improved Baselines with Momentum Contrastive Learning
Improved Baselines with Momentum Contrastive Learning
Xinlei Chen
Haoqi Fan
Ross B. Girshick
Kaiming He
SSL
238
3,359
0
09 Mar 2020
1