- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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1 Online-Ressource.
- Language
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Englisch
- Bibliographic citation
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Object embedding using an information geometrical perspective ; volume:6 ; number:2 ; day:27 ; month:7 ; year:2023 ; pages:435-462 ; date:11.2023
Information geometry ; 6, Heft 2 (27.7.2023), 435-462, 11.2023
- Creator
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Sugiura, Taiki
Murata, Noboru
- Contributor
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SpringerLink (Online service)
- DOI
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10.1007/s41884-023-00114-z
- URN
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urn:nbn:de:101:1-2024011721124083977214
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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15.08.2025, 7:27 AM CEST
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Associated
- Sugiura, Taiki
- Murata, Noboru
- SpringerLink (Online service)
Other Objects (12)
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Geometrical notes.
Geometrical notes.
Publisher Correction: Object embedding using an information geometrical perspective
Embedding theory as new geometrical mimetic gravity
Form, space and object : geometrical transformations in object recognition and categorization
On the geometrical theory of perspective.
SCGTracker: object feature embedding enhancement based on graph attention networks for multi-object tracking
Projection separability: A new approach to evaluate embedding algorithms in the geometrical space
Embedding Real-Time Critical Robotics Applications in an Object-Oriented Language
A geometrical perspective on explanations for deep neural networks
Learning General and Specific Embedding with Transformer for Few-Shot Object Detection
Depth perception in single rgb camera system using lens aperture and object size: a geometrical approach for depth estimation
Geometrical notes.
Geometrical notes.
Publisher Correction: Object embedding using an information geometrical perspective
Embedding theory as new geometrical mimetic gravity
Form, space and object : geometrical transformations in object recognition and categorization
On the geometrical theory of perspective.
SCGTracker: object feature embedding enhancement based on graph attention networks for multi-object tracking
Projection separability: A new approach to evaluate embedding algorithms in the geometrical space
Embedding Real-Time Critical Robotics Applications in an Object-Oriented Language
A geometrical perspective on explanations for deep neural networks
Learning General and Specific Embedding with Transformer for Few-Shot Object Detection
Depth perception in single rgb camera system using lens aperture and object size: a geometrical approach for depth estimation
Geometrical notes.