Zobrazeno 1 - 10
of 79
pro vyhledávání: '"Kuizhi Mei"'
Publikováno v:
智能科学与技术学报, Vol 4, Pp 308-323 (2022)
One of the limitations of robotics is that it is difficult for robots to adapt to fickle tasks.A robot will inevitably forget the knowledge from old environments or tasks when facing new environments or tasks.In order to summarize research in continu
Externí odkaz:
https://doaj.org/article/c56036ba03214b32ab14d95148a9d339
Publikováno v:
IEEE Access, Vol 10, Pp 10259-10272 (2022)
Simultaneous localization and mapping (SLAM) is considered as a key technique in augmented reality (AR), robotics and unmanned driving. In the field of SLAM, solutions based on monocular sensors have gradually become important due to their ability to
Externí odkaz:
https://doaj.org/article/2350fe9d741d4b31abe69b1d212286c3
Publikováno v:
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems. 42:630-643
Publikováno v:
IEEE Transactions on Very Large Scale Integration (VLSI) Systems. 30:1902-1915
Publikováno v:
Pattern Recognition Letters. 164:161-165
Publikováno v:
Neurocomputing. 497:76-85
Publikováno v:
2022 China Automation Congress (CAC).
Publikováno v:
2022 IEEE 16th International Conference on Solid-State & Integrated Circuit Technology (ICSICT).
Graph Convolutional Networks (GCNs) have attracted broad attention from industry and academia, for which GCNs have demonstrated powerful ability to model the irregular data, e.g., skeletal data and graph-structured data. The most existing effective m
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d4beed90fc3c62dcf994877f4dd16628
https://doi.org/10.21203/rs.3.rs-1807939/v1
https://doi.org/10.21203/rs.3.rs-1807939/v1
Publikováno v:
IEEE Transactions on Circuits and Systems for Video Technology. 31:890-904
The regression based deep neural networks have achieved state-of-the-arts performance on depth 3D hand pose estimation task. This paper focuses on improving the regression mapping between features and pose joints. Inspired by the distribution modelin