367 citations · 1k across the 6 of their papers we have counts for
7 papers
Learnable latent embeddings for joint behavioral and neural analysis
Steffen Schneider, Jin Hwa Lee, Mackenzie Weygandt Mathis
Mapping behavioral actions to neural activity is a fundamental goal of neuroscience. As our ability to record large neural and behavioral data increases, there is growing interest…
SuperAnimal pretrained pose estimation models for behavioral analysis
Shaokai Ye, Anastasiia Filippova, Jessy Lauer +5
Quantification of behavior is critical in applications ranging from neuroscience, veterinary medicine and animal conservation efforts. A common key step for behavioral analysis is…
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…
A Primer on Motion Capture with Deep Learning: Principles, Pitfalls and Perspectives
Alexander Mathis, Steffen Schneider, Jessy Lauer +1
Extracting behavioral measurements non-invasively from video is stymied by the fact that it is a hard computational problem. Recent advances in deep learning have tremendously adva…
vq-wav2vec: Self-Supervised Learning of Discrete Speech Representations
Alexei Baevski, Steffen Schneider, Michael Auli
We propose vq-wav2vec to learn discrete representations of audio segments through a wav2vec-style self-supervised context prediction task. The algorithm uses either a gumbel softma…
Pretraining boosts out-of-domain robustness for pose estimation
Alexander Mathis, Thomas Biasi, Steffen Schneider +4
Neural networks are highly effective tools for pose estimation. However, as in other computer vision tasks, robustness to out-of-domain data remains a challenge, especially for sma…
Context-based Normalization of Histological Stains using Deep Convolutional Features
Daniel Bug, Steffen Schneider, Anne Grote +4
While human observers are able to cope with variations in color and appearance of histological stains, digital pathology algorithms commonly require a well-normalized setting to ac…