Variational Multi-Prototype Encoder for Object Recognition Using Multiple Prototype Images

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....

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Veröffentlicht in:IEEE access 2022, Vol.10, p.19586-19598
Hauptverfasser: Kang, Jun Seok, Ahn, Sang Chul
Format: Artikel
Sprache:eng
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Zusammenfassung: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.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2022.3151856