Search Results for author: Jörn Ostermann

Found 7 papers, 3 papers with code

SegForestNet: Spatial-Partitioning-Based Aerial Image Segmentation

1 code implementation3 Feb 2023 Daniel Gritzner, Jörn Ostermann

Aerial image segmentation is the basis for applications such as automatically creating maps or tracking deforestation.

Image Segmentation Semantic Segmentation

Domain Adaptation for Head Pose Estimation Using Relative Pose Consistency

1 code implementation IEEE Transactions on Biometrics, Behavior, and Identity Science 2023 Felix Kuhnke, Jörn Ostermann

We propose a strategy to exploit the relative pose introduced by pose-altering augmentations between augmented image pairs, to allow the network to benefit from relative pose labels during training on unlabeled data.

Domain Adaptation Head Pose Estimation

Two-Stream Aural-Visual Affect Analysis in the Wild

1 code implementation9 Feb 2020 Felix Kuhnke, Lars Rumberg, Jörn Ostermann

In this work, we introduce our submission to the Affective Behavior Analysis in-the-wild (ABAW) 2020 competition.

Face Alignment Vocal Bursts Valence Prediction

HEVC Inter Coding Using Deep Recurrent Neural Networks and Artificial Reference Pictures

no code implementations5 Dec 2018 Felix Haub, Thorsten Laude, Jörn Ostermann

The efficiency of motion compensated prediction in modern video codecs highly depends on the available reference pictures.

Motion Compensation

Neural Network Compression using Transform Coding and Clustering

no code implementations NIPS Workshop CDNNRIA 2018 Thorsten Laude, Yannick Richter, Jörn Ostermann

With the deployment of neural networks on mobile devices and the necessity of transmitting neural networks over limited or expensive channels, the file size of the trained model was identified as bottleneck.

Clustering General Classification +2

Unsupervised Features for Facial Expression Intensity Estimation over Time

no code implementations2 May 2018 Maren Awiszus, Stella Graßhof, Felix Kuhnke, Jörn Ostermann

The proposed feature is compared to a state-of-the-art method for expression intensity estimation, which it outperforms.

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