How to add new knowledge to already trained deep learning models applied to semantic localization
The capacity of a robot to automatically adapt to new environments is crucial, especially in social robotics. Often, when these robots are deployed in home or office environments, they tend to fail because they lack the ability to adapt to new and continuously changing scenarios. In order to accompl...
שמור ב:
| מחבר ראשי: | |
|---|---|
| מחברים אחרים: | , , |
| פורמט: | article |
| שפה: | אנגלית |
| יצא לאור: |
2020
|
| נושאים: | |
| גישה מקוונת: | https://link.springer.com/article/10.1007/s10489-019-01517-1 https://ridda2.utp.ac.pa/handle/123456789/9445 |
| תגים: |
אין תגיות, היה/י הראשונ/ה לתייג את הרשומה!
|
פריטים דומים: How to add new knowledge to already trained deep learning models applied to semantic localization
- Robot Semantic Localization Through CNN Descriptors
- Semantic Localization of a Robot in a Real Home
- Multiagent System for Semantic Categorization of Places Mean the Use of Distributed Surveillance Cameras
- Semi-supervised 3D object recognition through CNN labeling
- Scene classification based on semantic labeling
- LexToMap: lexical-based topological mapping