Zobrazeno 1 - 10
of 4 482
pro vyhledávání: '"Stiefelhagen, P."'
Autor:
Schneider, David, Reiß, Simon, Kugler, Marco, Jaus, Alexander, Peng, Kunyu, Sutschet, Susanne, Sarfraz, M. Saquib, Matthiesen, Sven, Stiefelhagen, Rainer
Exploring the intricate dynamics between muscular and skeletal structures is pivotal for understanding human motion. This domain presents substantial challenges, primarily attributed to the intensive resources required for acquiring ground truth musc
Externí odkaz:
http://arxiv.org/abs/2411.00128
Autor:
Jaus, Alexander, Seibold, Constantin, Reiß, Simon, Marinov, Zdravko, Li, Keyi, Ye, Zeling, Krieg, Stefan, Kleesiek, Jens, Stiefelhagen, Rainer
We present Connected-Component~(CC)-Metrics, a novel semantic segmentation evaluation protocol, targeted to align existing semantic segmentation metrics to a multi-instance detection scenario in which each connected component matters. We motivate thi
Externí odkaz:
http://arxiv.org/abs/2410.18684
Autor:
Schneider, David, Sajadmanesh, Sina, Sehwag, Vikash, Sarfraz, Saquib, Stiefelhagen, Rainer, Lyu, Lingjuan, Sharma, Vivek
Publikováno v:
Proceedings of the 2nd International Workshop on Privacy-Preserving Computer Vision, ECCV 2024
Privacy-preserving computer vision is an important emerging problem in machine learning and artificial intelligence. The prevalent methods tackling this problem use differential privacy or anonymization and obfuscation techniques to protect the priva
Externí odkaz:
http://arxiv.org/abs/2410.17098
Autor:
Heinemann, Lena, Jaus, Alexander, Marinov, Zdravko, Kim, Moon, Spadea, Maria Francesca, Kleesiek, Jens, Stiefelhagen, Rainer
Within this work, we introduce LIMIS: The first purely language-based interactive medical image segmentation model. We achieve this by adapting Grounded SAM to the medical domain and designing a language-based model interaction strategy that allows r
Externí odkaz:
http://arxiv.org/abs/2410.16939
Autor:
Peng, Kunyu, Wen, Di, Yang, Kailun, Luo, Ao, Chen, Yufan, Fu, Jia, Sarfraz, M. Saquib, Roitberg, Alina, Stiefelhagen, Rainer
In Open-Set Domain Generalization (OSDG), the model is exposed to both new variations of data appearance (domains) and open-set conditions, where both known and novel categories are present at test time. The challenges of this task arise from the dua
Externí odkaz:
http://arxiv.org/abs/2409.17555
Autor:
Fervers, Florian, Bullinger, Sebastian, Bodensteiner, Christoph, Arens, Michael, Stiefelhagen, Rainer
This work presents a method that is able to predict the geolocation of a street-view photo taken in the wild within a state-sized search region by matching against a database of aerial reference imagery. We partition the search region into geographic
Externí odkaz:
http://arxiv.org/abs/2409.16763
Autor:
Jiang, Xin, Zheng, Junwei, Liu, Ruiping, Li, Jiahang, Zhang, Jiaming, Matthiesen, Sven, Stiefelhagen, Rainer
As Vision-Language Models (VLMs) advance, human-centered Assistive Technologies (ATs) for helping People with Visual Impairments (PVIs) are evolving into generalists, capable of performing multiple tasks simultaneously. However, benchmarking VLMs for
Externí odkaz:
http://arxiv.org/abs/2409.14215
In the field of autonomous driving, Bird's-Eye-View (BEV) perception has attracted increasing attention in the community since it provides more comprehensive information compared with pinhole front-view images and panoramas. Traditional BEV methods,
Externí odkaz:
http://arxiv.org/abs/2409.13912
In this work, we describe our approach to compete in the autoPET3 datacentric track. While conventional wisdom suggests that larger datasets lead to better model performance, recent studies indicate that excluding certain training samples can enhance
Externí odkaz:
http://arxiv.org/abs/2409.13548
Lightweight and effective models are essential for devices with limited resources, such as intelligent vehicles. Structured pruning offers a promising approach to model compression and efficiency enhancement. However, existing methods often tie pruni
Externí odkaz:
http://arxiv.org/abs/2408.03046