Osama Makansi

University of Freiburg | Albert-Ludwigs-Universität Freiburg · Department of Computer Science

Diffusion models excel at generating photorealistic images from text-queries. Naturally, many approaches have been proposed to use these generative abilities to augment training datasets for downstream tasks, such as classification. However, diffusion models are themselves trained on large noisily supervised, but nonetheless, annotated datasets. It...

Different urban microscale models exist to model street-level mean radiation temperature (Tmrt). However, these models are computationally expensive, albeit to varying degrees. We present a computational shortcut using a convolutional encoder-decoder network (U-Net) to predict pedestrian level (1.1 m a.g.l.) Tmrt at a building-resolved scale (1 × 1...

Future prediction is a fundamental principle of intelligence in which the future state of an environment is predicted given its past states. Accurate future prediction is relevant for applications that require safety in planning such as autonomous driving, robot navigation, or surveillance systems. The complexity of the task stems from the integrat...

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 successful reasoning about agent-agent interactio...

Medical image datasets are hard to collect, expensive to label, and often highly imbalanced. The last issue is underestimated, as typical average metrics hardly reveal that the often very important minority classes have a very low accuracy. In this paper, we address this problem by a feature embedding that balances the classes using contrastive lea...

Predicting the states of dynamic traffic actors into the fu-ture is important for autonomous systems to operate safelyand efficiently. Remarkably, the most critical scenarios aremuch less frequent and more complex than the uncriticalones. Therefore, uncritical cases dominate the prediction.In this paper, we address specifically the challenging sce-...

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 considerable field-of-view change due to the egomot...

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 and mostly yield a single hypothesis of the future...

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 prediction, which is vital information when building decisi...

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 applications. However, such networks require very large training datasets. Engineering the training data is difficult and/or laborious. This paper shows how to augment a n...

Recent work has shown that 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 the network estimate its local uncertainty about the correctness of its prediction, which is vital information...

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 information from multiple images. Typically, the images are related via optical flow and consecutive image warping. In this paper, we provide an end-to-end video super-resolut...