3 citations · 11 across the 13 of their papers we have counts for
15 papers
Continual Learning Should Move Beyond Incremental Classification
Rupert Mitchell, Antonio Alliegro, Raffaello Camoriano +17
Continual learning (CL) is the sub-field of machine learning concerned with accumulating knowledge in dynamic environments. So far, CL research has mainly focused on incremental cl…
DIAGen: Semantically Diverse Image Augmentation with Generative Models for Few-Shot Learning
Tobias Lingenberg, Markus Reuter, Gopika Sudhakaran +3
Simple data augmentation techniques, such as rotations and flips, are widely used to enhance the generalization power of computer vision models. However, these techniques often fai…
GLASS: Guided Latent Slot Diffusion for Object-Centric Learning
Krishnakant Singh, Simone Schaub-Meyer, Stefan Roth
Object-centric learning aims to decompose an input image into a set of meaningful object files (slots). These latent object representations enable a variety of downstream tasks. Ye…
Benchmarking the Attribution Quality of Vision Models
Robin Hesse, Simone Schaub-Meyer, Stefan Roth
Attribution maps are one of the most established tools to explain the functioning of computer vision models. They assign importance scores to input features, indicating how relevan…
Is Synthetic Data all We Need? Benchmarking the Robustness of Models Trained with Synthetic Images
Krishnakant Singh, Thanush Navaratnam, Jannik Holmer +2
A long-standing challenge in developing machine learning approaches has been the lack of high-quality labeled data. Recently, models trained with purely synthetic data, here termed…
Boosting Unsupervised Semantic Segmentation with Principal Mask Proposals
Oliver Hahn, Nikita Araslanov, Simone Schaub-Meyer +1
Unsupervised semantic segmentation aims to automatically partition images into semantically meaningful regions by identifying global semantic categories within an image corpus with…
FunnyBirds: A Synthetic Vision Dataset for a Part-Based Analysis of Explainable AI Methods
Robin Hesse, Simone Schaub-Meyer, Stefan Roth
The field of explainable artificial intelligence (XAI) aims to uncover the inner workings of complex deep neural models. While being crucial for safety-critical domains, XAI inhere…
Content-Adaptive Downsampling in Convolutional Neural Networks
Robin Hesse, Simone Schaub-Meyer, Stefan Roth
Many convolutional neural networks (CNNs) rely on progressive downsampling of their feature maps to increase the network's receptive field and decrease computational cost. However,…
Efficient Feature Extraction for High-resolution Video Frame Interpolation
Moritz Nottebaum, Stefan Roth, Simone Schaub-Meyer
Most deep learning methods for video frame interpolation consist of three main components: feature extraction, motion estimation, and image synthesis. Existing approaches are mainl…
-Flow: Joint Semantic and Style Editing of Facial Images
Krishnakant Singh, Simone Schaub-Meyer, Stefan Roth
The high-quality images yielded by generative adversarial networks (GANs) have motivated investigations into their application for image editing. However, GANs are often limited in…
Entropy-driven Unsupervised Keypoint Representation Learning in Videos
Ali Younes, Simone Schaub-Meyer, Georgia Chalvatzaki
Extracting informative representations from videos is fundamental for effectively learning various downstream tasks. We present a novel approach for unsupervised learning of meanin…
Fast Axiomatic Attribution for Neural Networks
Robin Hesse, Simone Schaub-Meyer, Stefan Roth
Mitigating the dependence on spurious correlations present in the training dataset is a quickly emerging and important topic of deep learning. Recent approaches include priors on t…
Dense Unsupervised Learning for Video Segmentation
Nikita Araslanov, Simone Schaub-Meyer, Stefan Roth
We present a novel approach to unsupervised learning for video object segmentation (VOS). Unlike previous work, our formulation allows to learn dense feature representations direct…
Deep Video Color Propagation
Simone Meyer, Victor Cornillère, Abdelaziz Djelouah +2
Traditional approaches for color propagation in videos rely on some form of matching between consecutive video frames. Using appearance descriptors, colors are then propagated both…
PhaseNet for Video Frame Interpolation
Simone Meyer, Abdelaziz Djelouah, Brian McWilliams +3
Most approaches for video frame interpolation require accurate dense correspondences to synthesize an in-between frame. Therefore, they do not perform well in challenging scenarios…