Search Results for author:
Found 9 papers, 4 papers with code
Patch n' Pack: NaViT, a Vision Transformer for any Aspect Ratio and Resolution
The ubiquitous and demonstrably suboptimal choice of resizing images to a fixed resolution before processing them with computer vision models has not yet been successfully challenged.
Image Captioners Are Scalable Vision Learners Too
We further analyze the effect of the model architecture and scale, as well as the pretraining data on the representation quality, and find that captioning exhibits the same or better scaling behavior along these axes.
Three Towers: Flexible Contrastive Learning with Pretrained Image Models
With 3T, we propose a more flexible strategy that allows the image tower to benefit from both pretrained embeddings and contrastive training.
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.
Scaling Vision Transformers to 22 Billion Parameters
The scaling of Transformers has driven breakthrough capabilities for language models.
Ranked #1 on
Linear-Probe Classification
on ImageNet
(using extra training data)
PaLI: A Jointly-Scaled Multilingual Language-Image Model
PaLI generates text based on visual and textual inputs, and with this interface performs many vision, language, and multimodal tasks, in many languages.
LiT: Zero-Shot Transfer with Locked-image text Tuning
This paper presents contrastive-tuning, a simple method employing contrastive training to align image and text models while still taking advantage of their pre-training.
How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers
Vision Transformers (ViT) have been shown to attain highly competitive performance for a wide range of vision applications, such as image classification, object detection and semantic image segmentation.
MLP-Mixer: An all-MLP Architecture for Vision
Convolutional Neural Networks (CNNs) are the go-to model for computer vision.
Ranked #18 on
Image Classification
on OmniBenchmark
