489 citations · 2.2k across the 126 of their papers we have counts for
19 papers · 1 filter
Indirect Local Attacks for Context-aware Semantic Segmentation Networks
Krishna Kanth Nakka, Mathieu Salzmann
Recently, deep networks have achieved impressive semantic segmentation performance, in particular thanks to their use of larger contextual information. In this paper, we show that…
ActiveMoCap: Optimized Viewpoint Selection for Active Human Motion Capture
Sena Kiciroglu, Helge Rhodin, Sudipta N. Sinha +2
The accuracy of monocular 3D human pose estimation depends on the viewpoint from which the image is captured. While freely moving cameras, such as on drones, provide control over t…
Contextually Plausible and Diverse 3D Human Motion Prediction
Sadegh Aliakbarian, Fatemeh Sadat Saleh, Lars Petersson +2
We tackle the task of diverse 3D human motion prediction, that is, forecasting multiple plausible future 3D poses given a sequence of observed 3D poses. In this context, a popular…
Training Provably Robust Models by Polyhedral Envelope Regularization
Chen Liu, Mathieu Salzmann, Sabine Süsstrunk
Training certifiable neural networks enables one to obtain models with robustness guarantees against adversarial attacks. In this work, we introduce a framework to bound the advers…
Shape Reconstruction by Learning Differentiable Surface Representations
Jan Bednarik, Shaifali Parashar, Erhan Gundogdu +2
Generative models that produce point clouds have emerged as a powerful tool to represent 3D surfaces, and the best current ones rely on learning an ensemble of parametric represent…
Using Depth for Pixel-Wise Detection of Adversarial Attacks in Crowd Counting
Weizhe Liu, Mathieu Salzmann, Pascal Fua
State-of-the-art methods for counting people in crowded scenes rely on deep networks to estimate crowd density. While effective, deep learning approaches are vulnerable to adversar…