Few-shot Object Recognition based on Three-Way Decision and Active Learning

Journal article

Li, B., Luo, S., Wang, J., Tian, L. and Chen, D. (2022). Few-shot Object Recognition based on Three-Way Decision and Active Learning. Visual Computer . 37.
AuthorsLi, B., Luo, S., Wang, J., Tian, L. and Chen, D.
Abstract

The problem of object recognition with few-shot or a few labeled samples is a very challenging research. Focusing on the problem of few shot object recognition, it is introduced a method of object recognition method based on active learning. To solve the recognition problem of unknown classes under the condition of few shot, based on the object recognition with active learning, we propose a sample selection method based on three-way decision. Firstly, we divide the unlabeled samples into three disjoint domains according to the recognition probability and the three-way decision threshold. Then, we use different query strategies for the three domains, as far as possible to select new samples of unknown classes from unlabeled samples, enrich the recognition types of the classification model, and optimize the performance of the classification model. Then, this paper introduces an algorithm for solving three-way decision threshold. Finally, simulation experiments verify the feasibility and effectiveness of the algorithm proposed in this chapter in object recognition.

Keywordsfew-shot object recognition; three-way decision; active learning; few-shot sample selection; object recognition
Year2022
JournalVisual Computer
Journal citation37
PublisherSpringer
ISSN0178-2789
Web address (URL)https://www.springer.com/journal/371
Publication dates
Print15 Feb 2022
Publication process dates
Accepted20 Jan 2022
Deposited31 Jan 2022
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This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/

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