1.4k citations · 3k across the 32 of their papers we have counts for
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A Simple Single-Scale Vision Transformer for Object Localization and Instance Segmentation
Wuyang Chen, Xianzhi Du, Fan Yang +8
This work presents a simple vision transformer design as a strong baseline for object localization and instance segmentation tasks. Transformers recently demonstrate competitive pe…
LiT: Zero-Shot Transfer with Locked-image text Tuning
Xiaohua Zhai, Xiao Wang, Basil Mustafa +4
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. In our emp…
MLP-Mixer: An all-MLP Architecture for Vision
Ilya Tolstikhin, Neil Houlsby, Alexander Kolesnikov +9
Convolutional Neural Networks (CNNs) are the go-to model for computer vision. Recently, attention-based networks, such as the Vision Transformer, have also become popular. In this…
How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers
Andreas Steiner, Alexander Kolesnikov, Xiaohua Zhai +3
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 seman…
Revisiting the Calibration of Modern Neural Networks
Matthias Minderer, Josip Djolonga, Rob Romijnders +5
Accurate estimation of predictive uncertainty (model calibration) is essential for the safe application of neural networks. Many instances of miscalibration in modern neural networ…
Knowledge distillation: A good teacher is patient and consistent
Lucas Beyer, Xiaohua Zhai, Amélie Royer +3
There is a growing discrepancy in computer vision between large-scale models that achieve state-of-the-art performance and models that are affordable in practical applications. In…