2.6k citations · 9.3k across the 89 of their papers we have counts for
15 papers · 1 filter
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…