Search Results for author: William Beluch

Found 8 papers, 3 papers with code

VSTAR: Generative Temporal Nursing for Longer Dynamic Video Synthesis

1 code implementation20 Mar 2024 Yumeng Li, William Beluch, Margret Keuper, Dan Zhang, Anna Khoreva

Despite tremendous progress in the field of text-to-video (T2V) synthesis, open-sourced T2V diffusion models struggle to generate longer videos with dynamically varying and evolving content.

Generative Temporal Nursing Text-to-Video Generation +1

Anomaly-Aware Semantic Segmentation via Style-Aligned OoD Augmentation

no code implementations19 Aug 2023 Dan Zhang, Kaspar Sakmann, William Beluch, Robin Hutmacher, Yumeng Li

Within the context of autonomous driving, encountering unknown objects becomes inevitable during deployment in the open world.

Anomaly Detection Autonomous Driving +3

A Survey of Methods for Addressing Class Imbalance in Deep-Learning Based Natural Language Processing

no code implementations10 Oct 2022 Sophie Henning, William Beluch, Alexander Fraser, Annemarie Friedrich

With this survey, the first overview on class imbalance in deep-learning based NLP, we provide guidance for NLP researchers and practitioners dealing with imbalanced data.

Benchmarking Data Augmentation

Improving Uncertainty of Deep Learning-based Object Classification on Radar Spectra using Label Smoothing

no code implementations27 Sep 2021 Kanil Patel, William Beluch, Kilian Rambach, Michael Pfeiffer, Bin Yang

The focus of this article is to learn deep radar spectra classifiers which offer robust real-time uncertainty estimates using label smoothing during training.

Decision Making

Investigation of Uncertainty of Deep Learning-based Object Classification on Radar Spectra

no code implementations1 Jun 2021 Kanil Patel, William Beluch, Kilian Rambach, Adriana-Eliza Cozma, Michael Pfeiffer, Bin Yang

Deep learning (DL) has recently attracted increasing interest to improve object type classification for automotive radar. In addition to high accuracy, it is crucial for decision making in autonomous vehicles to evaluate the reliability of the predictions; however, decisions of DL networks are non-transparent.

Autonomous Vehicles Decision Making +3

Multi-Class Uncertainty Calibration via Mutual Information Maximization-based Binning

1 code implementation ICLR 2021 Kanil Patel, William Beluch, Bin Yang, Michael Pfeiffer, Dan Zhang

The goal of this paper is to resolve the identified issues of HB in order to provide calibrated confidence estimates using only a small holdout calibration dataset for bin optimization while preserving multi-class ranking accuracy.


On-manifold Adversarial Data Augmentation Improves Uncertainty Calibration

no code implementations16 Dec 2019 Kanil Patel, William Beluch, Dan Zhang, Michael Pfeiffer, Bin Yang

Uncertainty estimates help to identify ambiguous, novel, or anomalous inputs, but the reliable quantification of uncertainty has proven to be challenging for modern deep networks.

Adversarial Attack Data Augmentation

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