  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.
