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Variational Multi-Prototype Encoder for Object Recognition Using Multiple Prototype Images
- Source :
- IEEE Access, Vol 10, Pp 19586-19598 (2022)
- Publication Year :
- 2022
- Publisher :
- IEEE, 2022.
-
Abstract
- In the recent research of Variational Prototyping-Encoder (VPE), the problem of classifying 2D flat objects of the unseen class has been addressed. VPE solves this problem by pre-learning the image translation task from real-world object images to their corresponding prototype images as a meta-task. VPE uses a single prototype for each object class. However, in general, a single prototype is not sufficient to represent a generic object class because the appearance can change significantly according to viewpoints and other factors. In this case, using VPE and a single prototype for each class in training can result in overfitting or performance degradation. One solution may be the use of multiple prototypes. However, this also requires costly sub-labeling for dividing the input class into smaller classes and assigning a prototype to each. Therefore, we propose a new learning method, the variational multi-prototype encoder (VaMPE), which can overcome the limitations of VPE and use multiple prototypes for each object class. The proposed method does not require additional sub-labeling other than simply adding multiple prototypes to each class. Through various experiments, we demonstrate that the proposed method outperforms VPE.
Details
- Language :
- English
- ISSN :
- 21693536
- Volume :
- 10
- Database :
- Directory of Open Access Journals
- Journal :
- IEEE Access
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.0924d57624fa1b5e1a0813fd95b4b
- Document Type :
- article
- Full Text :
- https://doi.org/10.1109/ACCESS.2022.3151856