15 citations · 17 across the 5 of their papers we have counts for
7 papers
MSViT: Dynamic Mixed-Scale Tokenization for Vision Transformers
Jakob Drachmann Havtorn, Amelie Royer, Tijmen Blankevoort +1
The input tokens to Vision Transformers carry little semantic meaning as they are defined as regular equal-sized patches of the input image, regardless of its content. However, pro…
Revisiting Single-gated Mixtures of Experts
Amelie Royer, Ilia Karmanov, Andrii Skliar +2
Mixture of Experts (MoE) are rising in popularity as a means to train extremely large-scale models, yet allowing for a reasonable computational cost at inference time. Recent state…
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…
A Flexible Selection Scheme for Minimum-Effort Transfer Learning
Amelie Royer, Christoph H. Lampert
Fine-tuning is a popular way of exploiting knowledge contained in a pre-trained convolutional network for a new visual recognition task. However, the orthogonal setting of transfer…
Localizing Grouped Instances for Efficient Detection in Low-Resource Scenarios
Amelie Royer, Christoph H. Lampert
State-of-the-art detection systems are generally evaluated on their ability to exhaustively retrieve objects densely distributed in the image, across a wide variety of appearances…
XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings
Amélie Royer, Konstantinos Bousmalis, Stephan Gouws +4
Style transfer usually refers to the task of applying color and texture information from a specific style image to a given content image while preserving the structure of the latte…
Probabilistic Image Colorization
Amelie Royer, Alexander Kolesnikov, Christoph H. Lampert
We develop a probabilistic technique for colorizing grayscale natural images. In light of the intrinsic uncertainty of this task, the proposed probabilistic framework has numerous…