1 code implementation • 25 Apr 2024 • Oliver Hahn, Nikita Araslanov, Simone Schaub-Meyer, Stefan Roth
Unsupervised semantic segmentation aims to automatically partition images into semantically meaningful regions by identifying global categories within an image corpus without any form of annotation.
no code implementations • 25 Mar 2024 • Simon Kiefhaber, Simon Niklaus, Feng Liu, Simone Schaub-Meyer
Video frame interpolation, the task of synthesizing new frames in between two or more given ones, is becoming an increasingly popular research target.
1 code implementation • ICCV 2023 • Robin Hesse, Simone Schaub-Meyer, Stefan Roth
Using our tools, we report results for 24 different combinations of neural models and XAI methods, demonstrating the strengths and weaknesses of the assessed methods in a fully automatic and systematic manner.
Explainable artificial intelligence Explainable Artificial Intelligence (XAI)
1 code implementation • 16 May 2023 • Robin Hesse, Simone Schaub-Meyer, Stefan Roth
Many convolutional neural networks (CNNs) rely on progressive downsampling of their feature maps to increase the network's receptive field and decrease computational cost.
1 code implementation • 25 Nov 2022 • Moritz Nottebaum, Stefan Roth, Simone Schaub-Meyer
The feature extraction layers help to compress the input and extract relevant information for the latter stages, such as motion estimation.
1 code implementation • 22 Nov 2022 • Krishnakant Singh, Simone Schaub-Meyer, Stefan Roth
In addition, methods that use semantic masks to edit images have difficulty preserving the identity and are unable to perform controlled style edits.
1 code implementation • 30 Sep 2022 • Ali Younes, Simone Schaub-Meyer, Georgia Chalvatzaki
Two original information-theoretic losses, computed from local entropy, guide our model to discover consistent keypoint representations; a loss that maximizes the spatial information covered by the keypoints and a loss that optimizes the keypoints' information transportation over time.
1 code implementation • NeurIPS 2021 • Robin Hesse, Simone Schaub-Meyer, Stefan Roth
Mitigating the dependence on spurious correlations present in the training dataset is a quickly emerging and important topic of deep learning.
1 code implementation • NeurIPS 2021 • Nikita Araslanov, Simone Schaub-Meyer, Stefan Roth
On established VOS benchmarks, our approach exceeds the segmentation accuracy of previous work despite using significantly less training data and compute power.