109 citations · 155 across the 16 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…
Residual Pattern Learning for Pixel-wise Out-of-Distribution Detection in Semantic Segmentation
Yuyuan Liu, Choubo Ding, Yu Tian +4
Semantic segmentation models classify pixels into a set of known (``in-distribution'') visual classes. When deployed in an open world, the reliability of these models depends on th…
Heatmap-based Out-of-Distribution Detection
Julia Hornauer, Vasileios Belagiannis
Our work investigates out-of-distribution (OOD) detection as a neural network output explanation problem. We learn a heatmap representation for detecting OOD images while visualizi…
Edge-Based Self-Supervision for Semi-Supervised Few-Shot Microscopy Image Cell Segmentation
Youssef Dawoud, Katharina Ernst, Gustavo Carneiro +1
Deep neural networks currently deliver promising results for microscopy image cell segmentation, but they require large-scale labelled databases, which is a costly and time-consumi…
Translation Consistent Semi-supervised Segmentation for 3D Medical Images
Yuyuan Liu, Yu Tian, Chong Wang +4
3D medical image segmentation methods have been successful, but their dependence on large amounts of voxel-level annotated data is a disadvantage that needs to be addressed given t…
ACPL: Anti-curriculum Pseudo-labelling for Semi-supervised Medical Image Classification
Fengbei Liu, Yu Tian, Yuanhong Chen +3
Effective semi-supervised learning (SSL) in medical image analysis (MIA) must address two challenges: 1) work effectively on both multi-class (e.g., lesion classification) and mult…
Perturbed and Strict Mean Teachers for Semi-supervised Semantic Segmentation
Yuyuan Liu, Yu Tian, Yuanhong Chen +3
Consistency learning using input image, feature, or network perturbations has shown remarkable results in semi-supervised semantic segmentation, but this approach can be seriously…
PropMix: Hard Sample Filtering and Proportional MixUp for Learning with Noisy Labels
Filipe R. Cordeiro, Vasileios Belagiannis, Ian Reid +1
The most competitive noisy label learning methods rely on an unsupervised classification of clean and noisy samples, where samples classified as noisy are re-labelled and "MixMatch…
ScanMix: Learning from Severe Label Noise via Semantic Clustering and Semi-Supervised Learning
Ragav Sachdeva, Filipe R Cordeiro, Vasileios Belagiannis +2
We propose a new training algorithm, ScanMix, that explores semantic clustering and semi-supervised learning (SSL) to allow superior robustness to severe label noise and competitiv…
LongReMix: Robust Learning with High Confidence Samples in a Noisy Label Environment
Filipe R. Cordeiro, Ragav Sachdeva, Vasileios Belagiannis +2
Deep neural network models are robust to a limited amount of label noise, but their ability to memorise noisy labels in high noise rate problems is still an open issue. The most co…
NVUM: Non-Volatile Unbiased Memory for Robust Medical Image Classification
Fengbei Liu, Yuanhong Chen, Yu Tian +4
Real-world large-scale medical image analysis (MIA) datasets have three challenges: 1) they contain noisy-labelled samples that affect training convergence and generalisation, 2) t…