Deep neural networks have demonstrated advanced abilities on various visual classification tasks, which heavily rely on the large-scale training samples with annotated ground-truth. However, it is unrealistic always to require such annotation in real-world applications. Recently, Few-Shot learning (FS), as an attempt to address the shortage of training samples, has made significant progress in generic classification tasks. Nonetheless, it is still challenging for current FS models to distinguish the subtle differences between fine-grained categories given limited training data. To filling the classification gap, in this paper, we address the Few-Shot Fine-Grained (FSFG) classification problem, which focuses on tackling the fine-grained classification under the challenging few-shot learning setting. A novel low-rank pairwise bilinear pooling operation is proposed to capture the nuanced differences between the support and query images for learning an effective distance metric. Moreover, a feature alignment layer is designed to match the support image features with query ones before the comparison. We name the proposed model Low-Rank Pairwise Alignment Bilinear Network (LRPABN), which is trained in an end-to-end fashion. Comprehensive experimental results on four widely used fine-grained classification datasets demonstrate that our LRPABN model achieves the superior performances compared to state-of-the-art methods.

中文翻译：

---

用于少镜头细粒度图像分类的低秩成对对齐双线性网络

深度神经网络在各种视觉分类任务上表现出先进的能力，这些任务严重依赖于带有标注的真实数据的大规模训练样本。然而，在实际应用中总是需要这样的注释是不现实的。最近，Few-Shot learning (FS) 作为一种解决训练样本短缺的尝试，在通用分类任务中取得了重大进展。尽管如此，对于当前的 FS 模型来说，在有限的训练数据下区分细粒度类别之间的细微差异仍然具有挑战性。为了填补分类空白，在本文中，我们解决了少样本细粒度 (FSFG) 分类问题，该问题侧重于在具有挑战性的少样本学习设置下解决细粒度分类问题。提出了一种新的低秩成对双线性池化操作来捕获支持图像和查询图像之间的细微差别，以学习有效的距离度量。此外，特征对齐层旨在在比较之前将支持图像特征与查询特征进行匹配。我们将提议的模型命名为低秩成对对齐双线性网络 (LRPABN)，它以端到端的方式进行训练。在四个广泛使用的细粒度分类数据集上的综合实验结果表明，与最先进的方法相比，我们的 LRPABN 模型实现了卓越的性能。特征对齐层旨在在比较之前将支持图像特征与查询特征进行匹配。我们将提议的模型命名为低秩成对对齐双线性网络 (LRPABN)，它以端到端的方式进行训练。在四个广泛使用的细粒度分类数据集上的综合实验结果表明，与最先进的方法相比，我们的 LRPABN 模型实现了卓越的性能。特征对齐层旨在在比较之前将支持图像特征与查询特征进行匹配。我们将提议的模型命名为低秩成对对齐双线性网络 (LRPABN)，它以端到端的方式进行训练。在四个广泛使用的细粒度分类数据集上的综合实验结果表明，与最先进的方法相比，我们的 LRPABN 模型实现了卓越的性能。