1 citations · 2 across the 9 of their papers we have counts for
11 papers
Importance-Aware Low-Rank Distillation of Diffusion Transformers
Denis Zavadski, Sebastian Heid, Damjan Kalšan +2
Diffusion Transformers (DiTs) have emerged as a dominant architecture for high-quality text-to-image generation, yet their scale poses challenges for efficient deployment. While tr…
Evaluating Object-Centric Models beyond Object Discovery
Krishnakant Singh, Simone Schaub-Meyer, Stefan Roth
Object-centric learning (OCL) aims to learn structured scene representations that support compositional generalization and robustness to out-of-distribution (OOD) data. However, OC…
MUFASA: A Multi-Layer Framework for Slot Attention
Sebastian Bock, Leonie Schüßler, Krishnakant Singh +2
Unsupervised object-centric learning (OCL) decomposes visual scenes into distinct entities. Slot attention is a popular approach that represents individual objects as latent vector…
Removing Cost Volumes from Optical Flow Estimators
Simon Kiefhaber, Stefan Roth, Simone Schaub-Meyer
Cost volumes are used in every modern optical flow estimator, but due to their computational and space complexity, they are often a limiting factor regarding both processing speed…
ART: Adaptive Relation Tuning for Generalized Relation Prediction
Gopika Sudhakaran, Hikaru Shindo, Patrick Schramowski +3
Visual relation detection (VRD) is the task of identifying the relationships between objects in a scene. VRD models trained solely on relation detection data struggle to generalize…
Efficient Masked Attention Transformer for Few-Shot Classification and Segmentation
Dustin Carrión-Ojeda, Stefan Roth, Simone Schaub-Meyer
Few-shot classification and segmentation (FS-CS) focuses on jointly performing multi-label classification and multi-class segmentation using few annotated examples. Although the cu…
Disentangling Polysemantic Channels in Convolutional Neural Networks
Robin Hesse, Jonas Fischer, Simone Schaub-Meyer +1
Mechanistic interpretability is concerned with analyzing individual components in a (convolutional) neural network (CNN) and how they form larger circuits representing decision mec…
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…