Search Results for author: Dominik Zietlow

Found 14 papers, 8 papers with code

Unsupervised Open-Vocabulary Object Localization in Videos

1 code implementation ICCV 2023 Ke Fan, Zechen Bai, Tianjun Xiao, Dominik Zietlow, Max Horn, Zixu Zhao, Carl-Johann Simon-Gabriel, Mike Zheng Shou, Francesco Locatello, Bernt Schiele, Thomas Brox, Zheng Zhang, Yanwei Fu, Tong He

In this paper, we show that recent advances in video representation learning and pre-trained vision-language models allow for substantial improvements in self-supervised video object localization.

Object Object Localization +1

Object-Centric Multiple Object Tracking

1 code implementation ICCV 2023 Zixu Zhao, Jiaze Wang, Max Horn, Yizhuo Ding, Tong He, Zechen Bai, Dominik Zietlow, Carl-Johann Simon-Gabriel, Bing Shuai, Zhuowen Tu, Thomas Brox, Bernt Schiele, Yanwei Fu, Francesco Locatello, Zheng Zhang, Tianjun Xiao

Unsupervised object-centric learning methods allow the partitioning of scenes into entities without additional localization information and are excellent candidates for reducing the annotation burden of multiple-object tracking (MOT) pipelines.

Multiple Object Tracking Object +3

Image retrieval outperforms diffusion models on data augmentation

no code implementations20 Apr 2023 Max F. Burg, Florian Wenzel, Dominik Zietlow, Max Horn, Osama Makansi, Francesco Locatello, Chris Russell

Many approaches have been proposed to use diffusion models to augment training datasets for downstream tasks, such as classification.

Data Augmentation Image Retrieval +2

Assaying Out-Of-Distribution Generalization in Transfer Learning

1 code implementation19 Jul 2022 Florian Wenzel, Andrea Dittadi, Peter Vincent Gehler, Carl-Johann Simon-Gabriel, Max Horn, Dominik Zietlow, David Kernert, Chris Russell, Thomas Brox, Bernt Schiele, Bernhard Schölkopf, Francesco Locatello

Since out-of-distribution generalization is a generally ill-posed problem, various proxy targets (e. g., calibration, adversarial robustness, algorithmic corruptions, invariance across shifts) were studied across different research programs resulting in different recommendations.

Adversarial Robustness Out-of-Distribution Generalization +1

Leveling Down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers

1 code implementation CVPR 2022 Dominik Zietlow, Michael Lohaus, Guha Balakrishnan, Matthäus Kleindessner, Francesco Locatello, Bernhard Schölkopf, Chris Russell

Algorithmic fairness is frequently motivated in terms of a trade-off in which overall performance is decreased so as to improve performance on disadvantaged groups where the algorithm would otherwise be less accurate.

Fairness

InvGAN: Invertible GANs

no code implementations8 Dec 2021 Partha Ghosh, Dominik Zietlow, Michael J. Black, Larry S. Davis, Xiaochen Hu

Our \textbf{InvGAN}, short for Invertible GAN, successfully embeds real images to the latent space of a high quality generative model.

Data Augmentation Image Inpainting +1

Demystifying Inductive Biases for $β$-VAE Based Architectures

no code implementations12 Feb 2021 Dominik Zietlow, Michal Rolinek, Georg Martius

By small, elaborate perturbations of existing datasets, we hide the convenient correlation structure that is easily exploited by a variety of architectures.

Disentanglement Inductive Bias

Machine learning time-local generators of open quantum dynamics

no code implementations21 Jan 2021 Paolo P. Mazza, Dominik Zietlow, Federico Carollo, Sabine Andergassen, Georg Martius, Igor Lesanovsky

Such evolution is typically emerging under the assumption of a weak coupling between the system and an infinitely large bath.

Quantum Physics Quantum Gases

Clearing the Path for Truly Semantic Representation Learning

no code implementations1 Jan 2021 Dominik Zietlow, Michal Rolinek, Georg Martius

The performance of $\beta$-Variational-Autoencoders ($\beta$-VAEs) and their variants on learning semantically meaningful, disentangled representations is unparalleled.

Disentanglement

Deep Graph Matching via Blackbox Differentiation of Combinatorial Solvers

5 code implementations25 Mar 2020 Michal Rolínek, Paul Swoboda, Dominik Zietlow, Anselm Paulus, Vít Musil, Georg Martius

Building on recent progress at the intersection of combinatorial optimization and deep learning, we propose an end-to-end trainable architecture for deep graph matching that contains unmodified combinatorial solvers.

Combinatorial Optimization Deep Learning +1

Variational Autoencoders Pursue PCA Directions (by Accident)

no code implementations CVPR 2019 Michal Rolinek, Dominik Zietlow, Georg Martius

The Variational Autoencoder (VAE) is a powerful architecture capable of representation learning and generative modeling.

Decoder Representation Learning

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