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基于提示学习的鸟类细粒度识别增量学习方法

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针对鸟类细粒度识别任务面临类间差异小和类内差异大的挑战,本文提出基于提示学习的鸟类细粒度识别增量学习方法.在增量学习模型中引入可学习的视觉提示,减少增量学习模型中灾难性遗忘.对于鸟类细粒度识别任务,引入不同粒度的文本信息作为增量学习模型中的文本提示,与视觉提示融合,实现由粗到细地学习不同鸟类的特征,提升鸟类细粒度识别精度.在CUB-200-2011数据集上数值实验表明,相比于其他增量学习模型,本文所提模型有更好的图像识别精度.对于一般的图像识别任务,本文所提模型在CIFAR-100和5-datasets数据集上具有更高的识别精度和更好的抗遗忘效果.
Incremental Learning Method for Fine-Grained Bird Recognition Based on Prompt Learning
Fine-grained bird recognition tasks frequently face the challenges of small interclass and large intraclass differences.In this study,we propose an incremental learning method for fine-grained bird recognition based on prompt learning.Learnable visual prompts are first introduced into the incremental learning model to alleviate the phenomenon of catastrophic forgetting in the incremental learning model.For fine-grained bird recognition,text information of different granularities is introduced as the text prompts in the incremental learning model,which are then fused with the visual prompts to learn the characteristics of different birds from coarse to fine and to improve fine-grained bird recognition accuracy.Numerical experiments on the CUB-200-2011 dataset show that the proposed model has better image recognition accuracy than other incremental learning models.For general image recognition tasks,proposed method exhibits higher recognition accuracy and better anti-forgetting on CIFAR-100 and 5-datasets.

fine-grained recognitionincremental learningprompt learningcatastrophic forgetting

朱桐、张海苗、邱钧

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北京信息科技大学应用数学研究所,北京 100101

细粒度识别 增量学习 提示学习 灾难性遗忘

2024

激光与光电子学进展
中国科学院上海光学精密机械研究所

激光与光电子学进展

CSTPCD北大核心
影响因子:1.153
ISSN:1006-4125
年,卷(期):2024.61(24)