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pro vyhledávání: '"Gomaa, Amr"'
Robust frame-wise embeddings are essential to perform video analysis and understanding tasks. We present a self-supervised method for representation learning based on aligning temporal video sequences. Our framework uses a transformer-based encoder t
Externí odkaz:
http://arxiv.org/abs/2409.04607
The rapid advancement of the automotive industry towards automated and semi-automated vehicles has rendered traditional methods of vehicle interaction, such as touch-based and voice command systems, inadequate for a widening range of non-driving rela
Externí odkaz:
http://arxiv.org/abs/2401.16123
Robot-assisted surgical systems have demonstrated significant potential in enhancing surgical precision and minimizing human errors. However, existing systems cannot accommodate individual surgeons' unique preferences and requirements. Additionally,
Externí odkaz:
http://arxiv.org/abs/2311.17693
Despite significant advances in gesture recognition technology, recognizing gestures in a driving environment remains challenging due to limited and costly data and its dynamic, ever-changing nature. In this work, we propose a model-adaptation approa
Externí odkaz:
http://arxiv.org/abs/2310.01659
There is an growing interest in using Large Language Models (LLMs) in multi-agent systems to tackle interactive real-world tasks that require effective collaboration and assessing complex situations. Yet, we still have a limited understanding of LLMs
Externí odkaz:
http://arxiv.org/abs/2309.17234
Autor:
Gomaa, Amr, Feld, Michael
Recent advances in machine learning, particularly deep learning, have enabled autonomous systems to perceive and comprehend objects and their environments in a perceptual subsymbolic manner. These systems can now perform object detection, sensor data
Externí odkaz:
http://arxiv.org/abs/2309.05787
Creating a diverse and comprehensive dataset of hand gestures for dynamic human-machine interfaces in the automotive domain can be challenging and time-consuming. To overcome this challenge, we propose using synthetic gesture datasets generated by vi
Externí odkaz:
http://arxiv.org/abs/2309.04421
Recent advances in machine learning models allowed robots to identify objects on a perceptual nonsymbolic level (e.g., through sensor fusion and natural language understanding). However, these primarily black-box learning models still lack interpreta
Externí odkaz:
http://arxiv.org/abs/2307.03853
Autor:
Gomaa, Amr
Publikováno v:
In Proceedings of the 2022 International Conference on Multimodal Interaction, pp. 690-695. 2022
With the recently increasing capabilities of modern vehicles, novel approaches for interaction emerged that go beyond traditional touch-based and voice command approaches. Therefore, hand gestures, head pose, eye gaze, and speech have been extensivel
Externí odkaz:
http://arxiv.org/abs/2211.03539
Publikováno v:
In Adjunct Proceedings of the 14th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, pp. 83-86. 2022
Many car accidents are caused by human distractions, including cognitive distractions. In-vehicle human-machine interfaces (HMIs) have evolved throughout the years, providing more and more functions. Interaction with the HMIs can, however, also lead
Externí odkaz:
http://arxiv.org/abs/2210.11271