Zobrazeno 1 - 10
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pro vyhledávání: '"Schneider David A"'
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:
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:
Peng, Kunyu, Yin, Cheng, Zheng, Junwei, Liu, Ruiping, Schneider, David, Zhang, Jiaming, Yang, Kailun, Sarfraz, M. Saquib, Stiefelhagen, Rainer, Roitberg, Alina
In real-world scenarios, human actions often fall outside the distribution of training data, making it crucial for models to recognize known actions and reject unknown ones. However, using pure skeleton data in such open-set conditions poses challeng
Externí odkaz:
http://arxiv.org/abs/2312.06330
Autor:
Chen, Yifei, Peng, Kunyu, Roitberg, Alina, Schneider, David, Zhang, Jiaming, Zheng, Junwei, Liu, Ruiping, Chen, Yufan, Yang, Kailun, Stiefelhagen, Rainer
To integrate self-supervised skeleton-based action recognition methods into autonomous robotic systems, it is crucial to consider adverse situations involving target occlusions. Such a scenario, despite its practical relevance, is rarely addressed in
Externí odkaz:
http://arxiv.org/abs/2309.12029
Autor:
Peng, Kunyu, Wen, Di, Schneider, David, Zhang, Jiaming, Yang, Kailun, Sarfraz, M. Saquib, Stiefelhagen, Rainer, Roitberg, Alina
Domain adaptation is essential for activity recognition to ensure accurate and robust performance across diverse environments, sensor types, and data sources. Unsupervised domain adaptation methods have been extensively studied, yet, they require lar
Externí odkaz:
http://arxiv.org/abs/2305.08420
Autor:
Peng, Kunyu, Schneider, David, Roitberg, Alina, Yang, Kailun, Zhang, Jiaming, Deng, Chen, Zhang, Kaiyu, Sarfraz, M. Saquib, Stiefelhagen, Rainer
In this paper, we tackle the new task of video-based Activated Muscle Group Estimation (AMGE) aiming at identifying active muscle regions during physical activity in the wild. To this intent, we provide the MuscleMap dataset featuring >15K video clip
Externí odkaz:
http://arxiv.org/abs/2303.00952
Although human action anticipation is a task which is inherently multi-modal, state-of-the-art methods on well known action anticipation datasets leverage this data by applying ensemble methods and averaging scores of unimodal anticipation networks.
Externí odkaz:
http://arxiv.org/abs/2210.12649
Modality selection is an important step when designing multimodal systems, especially in the case of cross-domain activity recognition as certain modalities are more robust to domain shift than others. However, selecting only the modalities which hav
Externí odkaz:
http://arxiv.org/abs/2208.09414
Domain shifts, such as appearance changes, are a key challenge in real-world applications of activity recognition models, which range from assistive robotics and smart homes to driver observation in intelligent vehicles. For example, while simulation
Externí odkaz:
http://arxiv.org/abs/2208.01910
Autor:
Roitberg, Alina, Peng, Kunyu, Marinov, Zdravko, Seibold, Constantin, Schneider, David, Stiefelhagen, Rainer
Visual recognition inside the vehicle cabin leads to safer driving and more intuitive human-vehicle interaction but such systems face substantial obstacles as they need to capture different granularities of driver behaviour while dealing with highly
Externí odkaz:
http://arxiv.org/abs/2204.04734