Zobrazeno 1 - 10
of 60
pro vyhledávání: '"Possegger, Horst"'
Autor:
Mirza, M. Jehanzeb, Zhao, Mengjie, Mao, Zhuoyuan, Doveh, Sivan, Lin, Wei, Gavrikov, Paul, Dorkenwald, Michael, Yang, Shiqi, Jha, Saurav, Wakaki, Hiromi, Mitsufuji, Yuki, Possegger, Horst, Feris, Rogerio, Karlinsky, Leonid, Glass, James
In this work, we propose a novel method (GLOV) enabling Large Language Models (LLMs) to act as implicit Optimizers for Vision-Langugage Models (VLMs) to enhance downstream vision tasks. Our GLOV meta-prompts an LLM with the downstream task descriptio
Externí odkaz:
http://arxiv.org/abs/2410.06154
For efficient and safe autonomous driving, it is essential that autonomous vehicles can predict the motion of other traffic agents. While highly accurate, current motion prediction models often impose significant challenges in terms of training resou
Externí odkaz:
http://arxiv.org/abs/2409.16154
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 large amounts of human-annotated data, which is expensive to obtain a
Externí odkaz:
http://arxiv.org/abs/2408.03790
State-of-the-art (SOTA) trackers have shown remarkable Multiple Object Tracking (MOT) performance when trained and evaluated on current benchmarks. However, these benchmarks primarily consist of clear scenarios, overlooking adverse atmospheric condit
Externí odkaz:
http://arxiv.org/abs/2404.10534
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 distribution with a neural network. This lets us estimate the likelih
Externí odkaz:
http://arxiv.org/abs/2403.14497
Autor:
Mirza, M. Jehanzeb, Karlinsky, Leonid, Lin, Wei, Doveh, Sivan, Micorek, Jakub, Kozinski, Mateusz, Kuehne, Hilde, Possegger, Horst
Prompt ensembling of Large Language Model (LLM) generated category-specific prompts has emerged as an effective method to enhance zero-shot recognition ability of Vision-Language Models (VLMs). To obtain these category-specific prompts, the present m
Externí odkaz:
http://arxiv.org/abs/2403.11755
Autor:
Schachner, Martin, Schneider, Bernd, Weissenbacher, Fabian, Kirillova, Nadezda, Possegger, Horst, Bischof, Horst, Klug, Corina
A better understanding of interactive pedestrian behavior in critical traffic situations is essential for the development of enhanced pedestrian safety systems. Real-world traffic observations play a decisive role in this, since they represent behavi
Externí odkaz:
http://arxiv.org/abs/2402.02533
Autor:
Schinagl, David, Krispel, Georg, Fruhwirth-Reisinger, Christian, Possegger, Horst, Bischof, Horst
Widely-used LiDAR-based 3D object detectors often neglect fundamental geometric information readily available from the object proposals in their confidence estimation. This is mostly due to architectural design choices, which were often adopted from
Externí odkaz:
http://arxiv.org/abs/2310.20319
Autor:
Mirza, M. Jehanzeb, Karlinsky, Leonid, Lin, Wei, Possegger, Horst, Feris, Rogerio, Bischof, Horst
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 visual recognition performance, these models still require tuning to better
Externí odkaz:
http://arxiv.org/abs/2309.06809
Autor:
Leitner, Stefan, Mirza, M. Jehanzeb, Lin, Wei, Micorek, Jakub, Masana, Marc, Kozinski, Mateusz, Possegger, Horst, Bischof, Horst
In autonomous driving scenarios, current object detection models show strong performance when tested in clear weather. However, their performance deteriorates significantly when tested in degrading weather conditions. In addition, even when adapted t
Externí odkaz:
http://arxiv.org/abs/2305.18953