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- Title
属性蒸馏的零样本识别方法.
- Authors
李厚君; 韦柏全
- Abstract
Zero-shot recognition is one of the most challenging tasks in the field of computer vision. The key problem is how to learn stable and transferable knowledge from the seen class. In order to increase the accuracy of zero-shot recognition, this paper carefully investigates the issue of zero-shot recognition and develops a straight forward and efficient attribute-distillation classifier based on the notion of knowledge distillation. It is consistent with how people generally understand things. It begins by obtaining extensive and precise visual features from the large model Vision Transformer, then uses the attribute idea to extract the attribute knowledge of objects before transforming to the task of classifying unseen classes. Public dataset experiments demonstrate that the proposed method has produced results that are competitive. Its recognition accuracy is slightly below that of the most recent attribute-guided algorithm, but it is still better than other conventional approaches, and its simple recognition architecture can achieve fast processing speed. Nevertheless, this research also makes the point that decreasing the sparsity of attribute descriptions and increasing multi-view high-definition photos will contribute to an increase in zero-shot recognition accuracy.
- Publication
Journal of Computer Engineering & Applications, 2024, Vol 60, Issue 9, p219
- ISSN
1002-8331
- Publication type
Article
- DOI
10.3778/j.issn.1002-8331.2212-0382