Centering the value of every modality: Towards efficient and resilient modality-agnostic semantic segmentation
Fusing an arbitrary number of modalities is vital for achieving robust multi-modal fusion of
semantic segmentation yet remains less explored to date. Recent endeavors regard RGB …
semantic segmentation yet remains less explored to date. Recent endeavors regard RGB …
[HTML][HTML] The Future of Intelligent Healthcare: A Systematic Analysis and Discussion on the Integration and Impact of Robots Using Large Language Models for …
The potential use of large language models (LLMs) in healthcare robotics can help address
the significant demand put on healthcare systems around the world with respect to an aging …
the significant demand put on healthcare systems around the world with respect to an aging …
[HTML][HTML] Study of Human–Robot Interactions for Assistive Robots Using Machine Learning and Sensor Fusion Technologies
R Raj, A Kos - Electronics, 2024 - mdpi.com
In recent decades, the potential of robots' understanding, perception, learning, and action
has been widely expanded due to the integration of artificial intelligence (AI) into almost …
has been widely expanded due to the integration of artificial intelligence (AI) into almost …
Omnibench: Towards the future of universal omni-language models
Y Li, G Zhang, Y Ma, R Yuan, K Zhu, H Guo… - ar** Review
P Müller, P Jahn - JMIR Human Factors, 2024 - humanfactors.jmir.org
Background Robotic technologies present challenges to health care professionals and are
therefore rarely used. Barriers such as lack of controllability and adaptability and complex …
therefore rarely used. Barriers such as lack of controllability and adaptability and complex …
Multi-Branch Generative Models for Multichannel Imaging with an Application to PET/CT Joint Reconstruction
This paper presents a proof-of-concept approach for learned synergistic reconstruction of
medical images using multi-branch generative models. Leveraging variational autoencoders …
medical images using multi-branch generative models. Leveraging variational autoencoders …