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Found 4 papers, 4 papers with code
MobileNetV4 - Universal Models for the Mobile Ecosystem
We present the latest generation of MobileNets, known as MobileNetV4 (MNv4), featuring universally efficient architecture designs for mobile devices.
R-MAE: Regions Meet Masked Autoencoders
In this work, we explore regions as a potential visual analogue of words for self-supervised image representation learning.
Hiera: A Hierarchical Vision Transformer without the Bells-and-Whistles
Modern hierarchical vision transformers have added several vision-specific components in the pursuit of supervised classification performance.
Ranked #1 on
Image Classification
on iNaturalist 2019
(using extra training data)
The effectiveness of MAE pre-pretraining for billion-scale pretraining
While MAE has only been shown to scale with the size of models, we find that it scales with the size of the training dataset as well.
Ranked #1 on
Few-Shot Image Classification
on ImageNet - 10-shot
(using extra training data)
