基于协作学习的双网络少拍图像分类

Min Xiong, Wenming Cao, Jianqi Zhong
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引用次数: 0

摘要

随着图像分类技术在计算机视觉领域的蓬勃发展,Few-shot learning (FSL)已成为解决小样本分类任务模型训练的研究热点。FSL旨在以较少的注释实现对新类别样本的高效识别和处理。以往的工作主要集中在基于单一模型的FSL信息提取上,缺乏对数据样本差异的区分。因此,我们提出了一种基于元学习的双模型和知识聚类方法,用于小样本图像分类,试图学习双模型之间的相关性,并捕获数据样本中嵌入的信息。此外,我们引入中心损失对同类样本进行聚类,并最大限度地提高类内相似性和类间差异性。我们在训练阶段采用了基于元学习的多任务。对于每个任务,双模型的训练分为两个阶段,在中心损失的指导下,两个阶段相互依赖。在第一阶段,用第二个模型的预测标签得到的软标签对第一个模型进行训练。第二阶段重复第一阶段的信息交换。我们发现主动模型的最优预测接近软标签和实际标签。在三个通用基准上的大量实验结果证明了我们提出的方法在少镜头分类任务上的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Collaborative Learning-based Dual Network for Few-Shot Image Classification
With the vigorous development of image classification technology in the field of computer vision, Few-shot learning (FSL) has become a research hotspot for solving classification task model training with a small number of samples. FSL aims to achieve efficient identification and processing of new category samples with few annotations. Previous works focus on information extraction based on one single model for FSL, lacking the distinction of the differences between data samples. Therefore, we present a meta-learning-based dual model with knowledge clustering for few-shot image classification, trying to learn the correlation between dual models and capture the information embedded in the data samples. In addition, we introduce the center loss to cluster the same sort of samples and to maximize the similarity among the intraclass and the difference among the inter-class. We adopt multiple tasks based on Meta-learning during the training stage. For each task, the training of dual models divides into two phases, which depend on each other under the guidance of the center loss. At the first phase, the first model is trained with a soft label obtained by the predicted label of the second model. The second phase repeats the information exchange of the first phase. We find that the optimal predictions of the active model are close to the soft and actual labels. Extensive experimental results on three general benchmarks illustrate the effectiveness of our proposed methods on few-shot classification tasks.
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