{"title":"探索少镜头分类的样本关系","authors":"","doi":"10.1016/j.patcog.2024.111089","DOIUrl":null,"url":null,"abstract":"<div><div>Few-shot classification (FSC) is a challenging problem, which aims to identify novel classes with limited samples. Most existing methods employ vanilla transfer learning or episodic meta-training to learn a feature extractor, and then measure the similarity between the query image and the few support examples of novel classes. However, these approaches merely learn feature representations from individual images, overlooking the exploration of the interrelationships among images. This neglect can hinder the attainment of more discriminative feature representations, thus limiting the potential improvement of few-shot classification performance. To address this issue, we propose a Sample Relationship Exploration (SRE) module comprising the Sample-level Attention (SA), Explicit Guidance (EG) and Channel-wise Adaptive Fusion (CAF) components, to learn discriminative category-related features. Specifically, we first employ the SA component to explore the similarity relationships among samples and obtain aggregated features of similar samples. Furthermore, to enhance the robustness of these features, we introduce the EG component to explicitly guide the learning of sample relationships by providing an ideal affinity map among samples. Finally, the CAF component is adopted to perform weighted fusion of the original features and the aggregated features, yielding category-related embeddings. The proposed method is a plug-and-play module which can be embedded into both transfer learning and meta-learning based few-shot classification frameworks. 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Furthermore, to enhance the robustness of these features, we introduce the EG component to explicitly guide the learning of sample relationships by providing an ideal affinity map among samples. Finally, the CAF component is adopted to perform weighted fusion of the original features and the aggregated features, yielding category-related embeddings. The proposed method is a plug-and-play module which can be embedded into both transfer learning and meta-learning based few-shot classification frameworks. Extensive experiments on benchmark datasets show that the proposed module can effectively improve the performance over baseline models, and also perform competitively against the state-of-the-art algorithms. 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引用次数: 0
摘要
快速分类(FSC)是一个具有挑战性的问题,其目的是利用有限的样本识别新类别。大多数现有方法都采用香草迁移学习或偶发元训练来学习特征提取器,然后测量查询图像与新类别的少数支持示例之间的相似性。然而,这些方法只是从单个图像中学习特征表征,忽略了对图像间相互关系的探索。这种忽视可能会阻碍获得更具区分性的特征表征,从而限制了少量图像分类性能的潜在提高。为解决这一问题,我们提出了一个样本关系探索(SRE)模块,该模块由样本级关注(SA)、显式引导(EG)和信道自适应融合(CAF)组件组成,用于学习与类别相关的判别特征。具体来说,我们首先利用 SA 组件探索样本之间的相似性关系,并获得相似样本的聚合特征。此外,为了增强这些特征的鲁棒性,我们引入了 EG 组件,通过提供样本间的理想亲和图来明确指导样本关系的学习。最后,我们采用 CAF 组件对原始特征和聚合特征进行加权融合,得到与类别相关的嵌入。所提出的方法是一个即插即用的模块,可以嵌入到基于迁移学习和元学习的少量分类框架中。在基准数据集上进行的大量实验表明,与基线模型相比,所提出的模块能有效提高性能,与最先进的算法相比也具有竞争力。源代码见 https://github.com/Chenguoz/SRE。
Exploring sample relationship for few-shot classification
Few-shot classification (FSC) is a challenging problem, which aims to identify novel classes with limited samples. Most existing methods employ vanilla transfer learning or episodic meta-training to learn a feature extractor, and then measure the similarity between the query image and the few support examples of novel classes. However, these approaches merely learn feature representations from individual images, overlooking the exploration of the interrelationships among images. This neglect can hinder the attainment of more discriminative feature representations, thus limiting the potential improvement of few-shot classification performance. To address this issue, we propose a Sample Relationship Exploration (SRE) module comprising the Sample-level Attention (SA), Explicit Guidance (EG) and Channel-wise Adaptive Fusion (CAF) components, to learn discriminative category-related features. Specifically, we first employ the SA component to explore the similarity relationships among samples and obtain aggregated features of similar samples. Furthermore, to enhance the robustness of these features, we introduce the EG component to explicitly guide the learning of sample relationships by providing an ideal affinity map among samples. Finally, the CAF component is adopted to perform weighted fusion of the original features and the aggregated features, yielding category-related embeddings. The proposed method is a plug-and-play module which can be embedded into both transfer learning and meta-learning based few-shot classification frameworks. Extensive experiments on benchmark datasets show that the proposed module can effectively improve the performance over baseline models, and also perform competitively against the state-of-the-art algorithms. The source code is available at https://github.com/Chenguoz/SRE.
期刊介绍:
The field of Pattern Recognition is both mature and rapidly evolving, playing a crucial role in various related fields such as computer vision, image processing, text analysis, and neural networks. It closely intersects with machine learning and is being applied in emerging areas like biometrics, bioinformatics, multimedia data analysis, and data science. The journal Pattern Recognition, established half a century ago during the early days of computer science, has since grown significantly in scope and influence.