2 citations · 2 across the 4 of their papers we have counts for
4 papers
From Keypoints to Predictive Distributions: Post-Hoc Uncertainty for YOLO-Pose Models
Alexej Klushyn, Juan Rivero Sesma, Florian Seligmann +3
YOLO-Pose models provide efficient keypoint localization, but do not quantify the associated spatial uncertainty. We introduce a lightweight post-hoc probabilistic extension that a…
VLA-FAIL: Efficient Task Failure Detection for Finetuned Vision-Language-Action Models
Florian Seligmann, Emiliyan Gospodinov, Enes Ulas Dincer +1
Vision-language-action models (VLAs) achieve state-of-the-art performance on many robotic manipulation tasks, yet they can still behave unpredictably in out-of-distribution scenari…
SEAR: Sample Efficient Action Chunking Reinforcement Learning
C. F. Maximilian Nagy, Onur Celik, Emiliyan Gospodinov +4
Action chunking improves exploration and accelerates value propagation in long-horizon reinforcement learning, but naively applying off-policy methods to the temporally extended ac…
Beyond Deep Ensembles: A Large-Scale Evaluation of Bayesian Deep Learning under Distribution Shift
Florian Seligmann, Philipp Becker, Michael Volpp +1
Bayesian deep learning (BDL) is a promising approach to achieve well-calibrated predictions on distribution-shifted data. Nevertheless, there exists no large-scale survey that eval…