6 citations · 6 across the 5 of their papers we have counts for
5 papers
A Hybrid Quantum Neural Network to Analyse Big Experimental Powder X-ray Diffraction Data
H. Dong, S. D. M. Jacques, M. Q. Hlatshwayo +4
Quantitative analysis of experimental powder X-ray diffraction data remains challenging when evaluating complex multiphase materials and noisy measurements. We introduce a hybrid q…
What DINO saw: ALiBi positional encoding reduces positional bias in Vision Transformers
Moritz Pawlowsky, Antonis Vamvakeros, Alexander Weiss +3
Vision transformers (ViTs) - especially feature foundation models like DINOv2 - learn rich representations useful for many downstream tasks. However, architectural choices (such as…
Maybe you don't need a U-Net: convolutional feature upsampling for materials micrograph segmentation
Ronan Docherty, Antonis Vamvakeros, Samuel J. Cooper
Feature foundation models - usually vision transformers - offer rich semantic descriptors of images, useful for downstream tasks such as (interactive) segmentation and object detec…
Upsampling DINOv2 features for unsupervised vision tasks and weakly supervised materials segmentation
Ronan Docherty, Antonis Vamvakeros, Samuel J. Cooper
The features of self-supervised vision transformers (ViTs) contain strong semantic and positional information relevant to downstream tasks like object localization and segmentation…
SAMBA: A Trainable Segmentation Web-App with Smart Labelling
Ronan Docherty, Isaac Squires, Antonis Vamvakeros +1
Segmentation is the assigning of a semantic class to every pixel in an image and is a prerequisite for various statistical analysis tasks in materials science, like phase quantific…