1 citations · 1 across the 8 of their papers we have counts for
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Vision-Language Guidance for LiDAR-based Unsupervised 3D Object Detection
Christian Fruhwirth-Reisinger, Wei Lin, Dušan Malić +2
Accurate 3D object detection in LiDAR point clouds is crucial for autonomous driving systems. To achieve state-of-the-art performance, the supervised training of detectors requires…
Into the Fog: Evaluating Robustness of Multiple Object Tracking
Nadezda Kirillova, M. Jehanzeb Mirza, Horst Bischof +1
State-of-the-art Multiple Object Tracking (MOT) approaches have shown remarkable performance when trained and evaluated on current benchmarks. However, these benchmarks primarily c…
MULDE: Multiscale Log-Density Estimation via Denoising Score Matching for Video Anomaly Detection
Jakub Micorek, Horst Possegger, Dominik Narnhofer +2
We propose a novel approach to video anomaly detection: we treat feature vectors extracted from videos as realizations of a random variable with a fixed distribution and model this…
GACE: Geometry Aware Confidence Enhancement for Black-Box 3D Object Detectors on LiDAR-Data
David Schinagl, Georg Krispel, Christian Fruhwirth-Reisinger +2
Widely-used LiDAR-based 3D object detectors often neglect fundamental geometric information readily available from the object proposals in their confidence estimation. This is most…
TAP: Targeted Prompting for Task Adaptive Generation of Textual Training Instances for Visual Classification
M. Jehanzeb Mirza, Leonid Karlinsky, Wei Lin +3
Vision and Language Models (VLMs), such as CLIP, have enabled visual recognition of a potentially unlimited set of categories described by text prompts. However, for the best visua…