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Found 10 papers, 5 papers with code
LocCa: Visual Pretraining with Location-aware Captioners
In this paper, we propose a simple visual pretraining method with location-aware captioners (LocCa).
A Study of Autoregressive Decoders for Multi-Tasking in Computer Vision
A key finding is that a small decoder learned on top of a frozen pretrained encoder works surprisingly well.
Tuning computer vision models with task rewards
Misalignment between model predictions and intended usage can be detrimental for the deployment of computer vision models.
UViM: A Unified Modeling Approach for Vision with Learned Guiding Codes
We introduce UViM, a unified approach capable of modeling a wide range of computer vision tasks.
Learning to Merge Tokens in Vision Transformers
Transformers are widely applied to solve natural language understanding and computer vision tasks.
Scaling Vision with Sparse Mixture of Experts
We present a Vision MoE (V-MoE), a sparse version of the Vision Transformer, that is scalable and competitive with the largest dense networks.
Ranked #1 on
Few-Shot Image Classification
on ImageNet - 5-shot
Deep Ensembles for Low-Data Transfer Learning
In the low-data regime, it is difficult to train good supervised models from scratch.
Ranked #6 on
Image Classification
on VTAB-1k
(using extra training data)
Which Model to Transfer? Finding the Needle in the Growing Haystack
Transfer learning has been recently popularized as a data-efficient alternative to training models from scratch, in particular for computer vision tasks where it provides a remarkably solid baseline.
Training general representations for remote sensing using in-domain knowledge
Automatically finding good and general remote sensing representations allows to perform transfer learning on a wide range of applications - improving the accuracy and reducing the required number of training samples.
Scalable Transfer Learning with Expert Models
We explore the use of expert representations for transfer with a simple, yet effective, strategy.
Ranked #11 on
Image Classification
on VTAB-1k
(using extra training data)
