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Found 4 papers, 3 papers with code
Vision-LSTM: xLSTM as Generic Vision Backbone
Transformers are widely used as generic backbones in computer vision, despite initially introduced for natural language processing.
Universal Physics Transformers: A Framework For Efficiently Scaling Neural Operators
This is of special interest since, akin to their numerical counterparts, different techniques are used across applications, even if the underlying dynamics of the systems are similar.
MIM-Refiner: A Contrastive Learning Boost from Intermediate Pre-Trained Representations
We introduce MIM (Masked Image Modeling)-Refiner, a contrastive learning boost for pre-trained MIM models.
Ranked #1 on
Image Clustering
on ImageNet
Contrastive Tuning: A Little Help to Make Masked Autoencoders Forget
In this work, we study how to combine the efficiency and scalability of MIM with the ability of ID to perform downstream classification in the absence of large amounts of labeled data.
Ranked #1 on
Image Clustering
on Imagenet-dog-15
(using extra training data)
