12 citations · 26 across the 11 of their papers we have counts for
16 papers
Prompt-Guided Image Editing with Masked Logit Nudging in Visual Autoregressive Models
Amir El-Ghoussani, Marc Hölle, Gustavo Carneiro +1
We address the problem of prompt-guided image editing in visual autoregressive models. Given a source image and a target text prompt, we aim to modify the source image according to…
Visual Autoregressive Modelling for Monocular Depth Estimation
Amir El-Ghoussani, André Kaup, Nassir Navab +2
We propose a monocular depth estimation method based on visual autoregressive (VAR) priors, offering an alternative to diffusion-based approaches. Our method adapts a large-scale t…
ItTakesTwo: Leveraging Peer Representations for Semi-supervised LiDAR Semantic Segmentation
Yuyuan Liu, Yuanhong Chen, Hu Wang +3
The costly and time-consuming annotation process to produce large training sets for modelling semantic LiDAR segmentation methods has motivated the development of semi-supervised l…
Consistency Regularisation for Unsupervised Domain Adaptation in Monocular Depth Estimation
Amir El-Ghoussani, Julia Hornauer, Gustavo Carneiro +1
In monocular depth estimation, unsupervised domain adaptation has recently been explored to relax the dependence on large annotated image-based depth datasets. However, this comes…
SelectNAdapt: Support Set Selection for Few-Shot Domain Adaptation
Youssef Dawoud, Gustavo Carneiro, Vasileios Belagiannis
Generalisation of deep neural networks becomes vulnerable when distribution shifts are encountered between train (source) and test (target) domain data. Few-shot domain adaptation…
Knowing What to Label for Few Shot Microscopy Image Cell Segmentation
Youssef Dawoud, Arij Bouazizi, Katharina Ernst +2
In microscopy image cell segmentation, it is common to train a deep neural network on source data, containing different types of microscopy images, and then fine-tune it using a su…