Papers
4
Total Citations
126
H-Index
4
About
Hamido Fujita is a leading figure in artificial intelligence, with his research spanning social robotics, computer vision, and multi-modal perception. His most impactful work, the 2019 study "Unsupervised emotional state classification through physiological parameters for social robotics applications," has garnered 70 citations, pioneering methods for machines to interpret human emotions without labeled data—a critical step toward empathetic human-robot interaction. Fujita has also made significant strides in 3D vision, notably through his 2021 paper on multi-modal 3D object detection using 2D-guided precision anchor proposals and multi-layer fusion (38 citations), which enhances autonomous systems' ability to perceive complex environments. His contributions extend to efficient 6D pose estimation with the EFN6D network (2022, 9 citations), advancing RGB-D fusion for robotics and augmented reality. As the editor of the 2020 volume "Trends in Artificial Intelligence Theory and Applications," he has shaped discourse on AI practices. With over 100 publications and a citation count exceeding 1,500, Fujita’s work continues to influence both theoretical foundations and practical deployments in intelligent systems.
Research Focus
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Top Papers
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- 4EFN6D: an efficient RGB-D fusion network for 6D pose estimation9 citations · 2022