198 citations · 225 across the 8 of their papers we have counts for
8 papers
Image retrieval outperforms diffusion models on data augmentation
Max F. Burg, Florian Wenzel, Dominik Zietlow +4
Many approaches have been proposed to use diffusion models to augment training datasets for downstream tasks, such as classification. However, diffusion models are themselves train…
You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction
Osama Makansi, Julius von Kügelgen, Francesco Locatello +4
Predicting the future trajectory of a moving agent can be easy when the past trajectory continues smoothly but is challenging when complex interactions with other agents are involv…
On Exposing the Challenging Long Tail in Future Prediction of Traffic Actors
Osama Makansi, Özgün Cicek, Yassine Marrakchi +1
Predicting the states of dynamic traffic actors into the future is important for autonomous systems to operate safelyand efficiently. Remarkably, the most critical scenarios aremuc…
Multimodal Future Localization and Emergence Prediction for Objects in Egocentric View with a Reachability Prior
Osama Makansi, Özgün Cicek, Kevin Buchicchio +1
In this paper, we investigate the problem of anticipating future dynamics, particularly the future location of other vehicles and pedestrians, in the view of a moving vehicle. We a…
Overcoming Limitations of Mixture Density Networks: A Sampling and Fitting Framework for Multimodal Future Prediction
Osama Makansi, Eddy Ilg, Özgün Cicek +1
Future prediction is a fundamental principle of intelligence that helps plan actions and avoid possible dangers. As the future is uncertain to a large extent, modeling the uncertai…
FusionNet and AugmentedFlowNet: Selective Proxy Ground Truth for Training on Unlabeled Images
Osama Makansi, Eddy Ilg, Thomas Brox
Recent work has shown that convolutional neural networks (CNNs) can be used to estimate optical flow with high quality and fast runtime. This makes them preferable for real-world a…
Uncertainty Estimates and Multi-Hypotheses Networks for Optical Flow
Eddy Ilg, Özgün Çiçek, Silvio Galesso +4
Optical flow estimation can be formulated as an end-to-end supervised learning problem, which yields estimates with a superior accuracy-runtime tradeoff compared to alternative met…
End-to-End Learning of Video Super-Resolution with Motion Compensation
Osama Makansi, Eddy Ilg, Thomas Brox
Learning approaches have shown great success in the task of super-resolving an image given a low resolution input. Video super-resolution aims for exploiting additionally the infor…