用子空间分解实现仅凭一张图识别新类别
Transductive One-Shot Learning Meet Subspace Decomposition
- 将图像分解为子空间基元的线性组合，挖掘共性特征
- 在多个数据集上仅用一张标注图即可准确识别新类
- 适合需要快速适应新类别的少样本场景
一对一学习旨在基于单张标注图像，将预训练模型适配以识别新引入且未见过的类别。尽管存在各种少样本和零样本学习变体，但一对学习因能从单一人工标注图像中泛化知识到未知类别，仍是极具挑战性且关键的问题。本文提出一种归纳式一对一学习方法，利用子空间分解技术，融合支持集中的标注图像与查询集中的未标注图像信息。这些图像被分解为表示由更小子空间捕捉的基元的潜在变量的线性组合。通过将查询集图像表示为这些潜在基元的线性组合，可将单个支持集图像的标签传播至共享相似基元组合的查询图像。通过对多种神经网络特征提取器及数据集的综合定量分析，证明该方法仅需一张标注图像即可有效泛化至新类别。
原文摘要 · Abstract (English)
One-shot learning focuses on adapting pretrained models to recognize newly introduced and unseen classes based on a single labeled image. While variations of few-shot and zero-shot learning exist, one-shot learning remains a challenging yet crucial problem due to its ability to generalize knowledge to unseen classes from just one human-annotated image. In this paper, we introduce a transductive one-shot learning approach that employs subspace decomposition to utilize the information from labeled images in the support set and unlabeled images in the query set. These images are decomposed into a linear combination of latent variables representing primitives captured by smaller subspaces. By representing images in the query set as linear combinations of these latent primitives, we can propagate the label from a single image in the support set to query images that share similar combinations of primitives. Through a comprehensive quantitative analysis across various neural network feature extractors and datasets, we demonstrate that our approach can effectively generalize to novel classes from just one labeled image.
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