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Osama Makansi

5 accepted papers

2022

You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction

Osama Makansi, Julius Von Kügelgen, Francesco Locatello, Peter Vincent Gehler, Dominik Janzing, Thomas Brox +1
ICLR 2022poster

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 involved. Recent deep learning approaches for trajectory prediction show promising performance and partially attribute this to…

Cited by 41Source ↗PDF ↗Scholar ↗
2021

On Exposing the Challenging Long Tail in Future Prediction of Traffic Actors

Osama Makansi, Özgün Çiçek, Yassine Marrakchi, Thomas Brox
ICCV 2021poster

Predicting the future states of dynamic traffic actors enables autonomous systems to avoid accidents and operate safely. Remarkably, the most critical scenarios are much less frequent and more complex than the uncritical ones. Therefore, uncritical cases dominate the prediction. In this paper, we ad…

Cited by 57PDF ↗code ↗Scholar ↗
2020

Multimodal Future Localization and Emergence Prediction for Objects in Egocentric View With a Reachability Prior

Osama Makansi, Ozgun Cicek, Kevin Buchicchio, Thomas Brox
CVPR 2020poster

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 approach two fundamental challenges: (1) the partial visibility due to the egocentric view with a single RGB camera and c…

Cited by 49PDF ↗code ↗Scholar ↗
2019

Overcoming Limitations of Mixture Density Networks: A Sampling and Fitting Framework for Multimodal Future Prediction

Osama Makansi, Eddy Ilg, Ozgun Cicek, Thomas Brox
CVPR 2019poster

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 uncertainty and multimodality of the future states is of great relevance. Existing approaches are rather limited in this regard…

Cited by 241PDF ↗code ↗Scholar ↗
2018

Uncertainty Estimates and Multi-Hypotheses Networks for Optical Flow

Eddy Ilg, Ozgun Cicek, Silvio Galesso, Aaron Klein, Osama Makansi, Frank Hutter +1
ECCV 2018poster

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 methodology. In this paper, we make such networks estimate their local uncertainty about the correctness of their predictio…

Cited by 270Source ↗PDF ↗Scholar ↗