Zobrazeno 1 - 10
of 4 674
pro vyhledávání: '"SCHNEIDER, DAVID"'
Autor:
Patwari, Kartik, Schneider, David, Sun, Xiaoxiao, Chuah, Chen-Nee, Lyu, Lingjuan, Sharma, Vivek
Growing privacy concerns and regulations like GDPR and CCPA necessitate pseudonymization techniques that protect identity in image datasets. However, retaining utility is also essential. Traditional methods like masking and blurring degrade quality a
Externí odkaz:
http://arxiv.org/abs/2412.06248
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
Privacy-preserving computer vision is an important emerging problem in machine learning and artificial intelligence. Prevalent methods tackling this problem use differential privacy (DP) or obfuscation techniques to protect the privacy of individuals
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