2.6k citations · 9.3k across the 89 of their papers we have counts for
104 papers
Anomaly Detection with Conditioned Denoising Diffusion Models
Arian Mousakhan, Thomas Brox, Jawad Tayyub
Traditional reconstruction-based methods have struggled to achieve competitive performance in anomaly detection. In this paper, we introduce Denoising Diffusion Anomaly Detection (…
Best Practices in Active Learning for Semantic Segmentation
Sudhanshu Mittal, Joshua Niemeijer, Jörg P. Schäfer +1
Active learning is particularly of interest for semantic segmentation, where annotations are costly. Previous academic studies focused on datasets that are already very diverse and…
Open-vocabulary Attribute Detection
María A. Bravo, Sudhanshu Mittal, Simon Ging +1
Vision-language modeling has enabled open-vocabulary tasks where predictions can be queried using any text prompt in a zero-shot manner. Existing open-vocabulary tasks focus on obj…
Construction of Hierarchical Neural Architecture Search Spaces based on Context-free Grammars
Simon Schrodi, Danny Stoll, Binxin Ru +3
The discovery of neural architectures from simple building blocks is a long-standing goal of Neural Architecture Search (NAS). Hierarchical search spaces are a promising step towar…
SF2SE3: Clustering Scene Flow into SE(3)-Motions via Proposal and Selection
Leonhard Sommer, Philipp Schröppel, Thomas Brox
We propose SF2SE3, a novel approach to estimate scene dynamics in form of a segmentation into independently moving rigid objects and their SE(3)-motions. SF2SE3 operates on two con…
A Benchmark and a Baseline for Robust Multi-view Depth Estimation
Philipp Schröppel, Jan Bechtold, Artemij Amiranashvili +1
Recent deep learning approaches for multi-view depth estimation are employed either in a depth-from-video or a multi-view stereo setting. Despite different settings, these approach…
Bridging the Gap to Real-World Object-Centric Learning
Maximilian Seitzer, Max Horn, Andrii Zadaianchuk +8
Humans naturally decompose their environment into entities at the appropriate level of abstraction to act in the world. Allowing machine learning algorithms to derive this decompos…
Probing Contextual Diversity for Dense Out-of-Distribution Detection
Silvio Galesso, Maria Alejandra Bravo, Mehdi Naouar +1
Detection of out-of-distribution (OoD) samples in the context of image classification has recently become an area of interest and active study, along with the topic of uncertainty…
RobotIO: A Python Library for Robot Manipulation Experiments
Lukas Hermann, Max Argus, Adrian Roefer +2
Setting up robot environments to quickly test newly developed algorithms is still a difficult and time consuming process. This presents a significant hurdle to researchers interest…
Pixel-level Correspondence for Self-Supervised Learning from Video
Yash Sharma, Yi Zhu, Chris Russell +1
While self-supervised learning has enabled effective representation learning in the absence of labels, for vision, video remains a relatively untapped source of supervision. To add…
Assaying Out-Of-Distribution Generalization in Transfer Learning
Florian Wenzel, Andrea Dittadi, Peter Vincent Gehler +9
Since out-of-distribution generalization is a generally ill-posed problem, various proxy targets (e.g., calibration, adversarial robustness, algorithmic corruptions, invariance acr…
Unsupervised Semantic Segmentation with Self-supervised Object-centric Representations
Andrii Zadaianchuk, Matthaeus Kleindessner, Yi Zhu +2
In this paper, we show that recent advances in self-supervised feature learning enable unsupervised object discovery and semantic segmentation with a performance that matches the s…
Conditional Visual Servoing for Multi-Step Tasks
Sergio Izquierdo, Max Argus, Thomas Brox
Visual Servoing has been effectively used to move a robot into specific target locations or to track a recorded demonstration. It does not require manual programming, but it is typ…
Localized Vision-Language Matching for Open-vocabulary Object Detection
Maria A. Bravo, Sudhanshu Mittal, Thomas Brox
In this work, we propose an open-vocabulary object detection method that, based on image-caption pairs, learns to detect novel object classes along with a given set of known classe…
NeuRL: Closed-form Inverse Reinforcement Learning for Neural Decoding
Gabriel Kalweit, Maria Kalweit, Mansour Alyahyay +6
Current neural decoding methods typically aim at explaining behavior based on neural activity via supervised learning. However, since generally there is a strong connection between…
Neural Architecture Search for Dense Prediction Tasks in Computer Vision
Thomas Elsken, Arber Zela, Jan Hendrik Metzen +4
The success of deep learning in recent years has lead to a rising demand for neural network architecture engineering. As a consequence, neural architecture search (NAS), which aims…
Ranking Info Noise Contrastive Estimation: Boosting Contrastive Learning via Ranked Positives
David T. Hoffmann, Nadine Behrmann, Juergen Gall +2
This paper introduces Ranking Info Noise Contrastive Estimation (RINCE), a new member in the family of InfoNCE losses that preserves a ranked ordering of positive samples. In contr…
Search for temporal cell segmentation robustness in phase-contrast microscopy videos
Estibaliz Gómez-de-Mariscal, Hasini Jayatilaka, Özgün Çiçek +3
Studying cell morphology changes in time is critical to understanding cell migration mechanisms. In this work, we present a deep learning-based workflow to segment cancer cells emb…
You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction
Osama Makansi, Julius von Kügelgen, Francesco Locatello +4
Predicting the future trajectory of a moving agent can be easy when the past trajectory continues smoothly but is challenging when complex interactions with other agents are involv…
Unsupervised Object Learning via Common Fate
Matthias Tangemann, Steffen Schneider, Julius von Kügelgen +6
Learning generative object models from unlabelled videos is a long standing problem and required for causal scene modeling. We decompose this problem into three easier subtasks, an…
CrossCLR: Cross-modal Contrastive Learning For Multi-modal Video Representations
Mohammadreza Zolfaghari, Yi Zhu, Peter Gehler +1
Contrastive learning allows us to flexibly define powerful losses by contrasting positive pairs from sets of negative samples. Recently, the principle has also been used to learn c…
Multi-headed Neural Ensemble Search
Ashwin Raaghav Narayanan, Arber Zela, Tonmoy Saikia +2
Ensembles of CNN models trained with different seeds (also known as Deep Ensembles) are known to achieve superior performance over a single copy of the CNN. Neural Ensemble Search…
Contrastive Representation Learning for Hand Shape Estimation
Christian Zimmermann, Max Argus, Thomas Brox
This work presents improvements in monocular hand shape estimation by building on top of recent advances in unsupervised learning. We extend momentum contrastive learning and contr…
Test-Time Adaptation to Distribution Shift by Confidence Maximization and Input Transformation
Chaithanya Kumar Mummadi, Robin Hutmacher, Kilian Rambach +3
Deep neural networks often exhibit poor performance on data that is unlikely under the train-time data distribution, for instance data affected by corruptions. Previous works demon…