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GroupEnsemble: Efficient Uncertainty Estimation for DETR-based Object Detection
Yutong Yang, Katarina Popović, Julian Wiederer +3
Detection Transformer (DETR) and its variants show strong performance on object detection, a key task for autonomous systems. However, a critical limitation of these models is that…
LSA: Localized Semantic Alignment for Enhancing Temporal Consistency in Traffic Video Generation
Mirlan Karimov, Teodora Spasojevic, Markus Braun +3
Controllable video generation has emerged as a versatile tool for autonomous driving, enabling realistic synthesis of traffic scenarios. However, existing methods depend on control…
Diffusion Model Guided Sampling with Pixel-Wise Aleatoric Uncertainty Estimation
Michele De Vita, Vasileios Belagiannis
Despite the remarkable progress in generative modelling, current diffusion models lack a quantitative approach to assess image quality. To address this limitation, we propose to es…
Data-Free Backbone Fine-Tuning for Pruned Neural Networks
Adrian Holzbock, Achyut Hegde, Klaus Dietmayer +1
Model compression techniques reduce the computational load and memory consumption of deep neural networks. After the compression operation, e.g. parameter pruning, the model is nor…
RESET: Revisiting Trajectory Sets for Conditional Behavior Prediction
Julian Schmidt, Pascal Huissel, Julian Wiederer +3
It is desirable to predict the behavior of traffic participants conditioned on different planned trajectories of the autonomous vehicle. This allows the downstream planner to estim…
Multi-Task Edge Prediction in Temporally-Dynamic Video Graphs
Osman Ülger, Julian Wiederer, Mohsen Ghafoorian +2
Graph neural networks have shown to learn effective node representations, enabling node-, link-, and graph-level inference. Conventional graph networks assume static relations betw…