Our research focuses on few-shot fine-grained image classification, which faces two major challenges: appearance similarity of fine-grained objects and limited number of samples. To preserve the appearance details of images, traditional feature reconstruction networks usually enhance the representation ability of key features by spatial feature reconstruction and minimizing the reconstruction error. However, we find that relying solely on a single type of feature is insufficient for accurately capturing inter-class differences of fine-grained objects in scenarios with limited samples. In contrast, the introduction of channel features provides additional information dimensions, aiding in better understanding and distinguishing the inter-class differences of fine-grained objects. Therefore, in this paper, we design a new Hybrid Feature Collaborative Reconstruction Network (HFCR-Net) for few-shot fine-grained image classification, which includes a Hybrid Feature Fusion Process (HFFP) and a Hybrid Feature Reconstruction Process (HFRP). In HFRP, we fuse the channel features and the spatial features. Through dynamic weight adjustment, we aggregate the spatial dependencies between arbitrary two positions and the correlations between different channels of each image to increase the inter-class differences. Additionally, we introduce the reconstruction of channel dimension in HFRP. Through the collaborative reconstruction of channel dimension and spatial dimension, the inter-class differences are further increased in the process of support-to-query reconstruction, while the intra-class differences are reduced in the process of query-to-support reconstruction. Ultimately, our extensive experiments on three widely used fine-grained datasets demonstrate the effectiveness and superiority of our approach.

翻译：本研究聚焦于少样本细粒度图像分类，其面临两大挑战：细粒度对象的外观相似性以及样本数量有限。为保留图像的细节外观，传统的特征重构网络通常通过空间特征重构和最小化重构误差来增强关键特征的表示能力。然而，我们发现，在样本有限的场景下，仅依赖单一类型的特征不足以准确捕捉细粒度对象的类间差异。相比之下，通道特征的引入提供了额外的信息维度，有助于更好地理解和区分细粒度对象的类间差异。因此，本文设计了一种用于少样本细粒度图像分类的新型混合特征协同重构网络（HFCR-Net），该网络包含混合特征融合过程（HFFP）和混合特征重构过程（HFRP）。在HFFP中，我们融合通道特征与空间特征。通过动态权重调整，我们聚合了任意两个位置之间的空间依赖关系以及每张图像不同通道之间的相关性，以增大类间差异。此外，我们在HFRP中引入了通道维度的重构。通过通道维度与空间维度的协同重构，在支持集到查询集的重构过程中进一步增大了类间差异，同时在查询集到支持集的重构过程中减小了类内差异。最终，我们在三个广泛使用的细粒度数据集上进行的大量实验证明了我们方法的有效性和优越性。