Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Kelm, André"'
Autor:
Kelm, André, Hannemann, Niels, Heberle, Bruno, Schmidt, Lucas, Rolff, Tim, Wilms, Christian, Yaghoubi, Ehsan, Frintrop, Simone
This study introduces a novel expert generation method that dynamically reduces task and computational complexity without compromising predictive performance. It is based on a new hierarchical classification network topology that combines sequential
Externí odkaz:
http://arxiv.org/abs/2403.05601
Deep learning object detection methods, like YOLOv5, are effective in identifying maritime vessels but often lack detailed information important for practical applications. In this paper, we addressed this problem by developing a technique that fuses
Externí odkaz:
http://arxiv.org/abs/2312.05270
Autor:
Kelm, André Peter, Hannemann, Niels, Heberle, Bruno, Schmidt, Lucas, Rolff, Tim, Wilms, Christian, Yaghoubi, Ehsan, Frintrop, Simone
This paper introduces a novel network topology that seamlessly integrates dynamic inference cost with a top-down attention mechanism, addressing two significant gaps in traditional deep learning models. Drawing inspiration from human perception, we c
Externí odkaz:
http://arxiv.org/abs/2308.05128
Autor:
Kelm, André Peter, Zölzer, Udo
We develop a new contour tracing algorithm to enhance the results of the latest object contour detectors. The goal is to achieve a perfectly closed, 1 pixel wide and detailed object contour, since this type of contour could be analyzed using methods
Externí odkaz:
http://arxiv.org/abs/2004.06587
A ResNet-based multi-path refinement CNN is used for object contour detection. For this task, we prioritise the effective utilization of the high-level abstraction capability of a ResNet, which leads to state-of-the-art results for edge detection. Ke
Externí odkaz:
http://arxiv.org/abs/1904.13353