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.

我们的研究聚焦于少样本细粒度图像分类，主要面临两个挑战：细粒度对象的外观相似性和样本数量有限。为了保留图像的外观细节，传统的特征重建网络通常通过空间特征重建和最小化重建误差来增强关键特征的表示能力。然而，我们发现在限制样本情景下，仅依赖于单一类型的特征不能准确捕捉细粒度对象的跨类别差异。相反，引入通道特征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之间的跨类别差异。因此，在本文中，我们设计了一个新的混合特征协同重建网络（HFCR-Net）用于少样本细粒度图像分类，其中包括混合特征融合过程（HFFP）和混合特征重建过程（HFRP）。在 HFRP 中，我们融合通道特征和空间特征。通过动态权重调整，我们汇总任意两个位置之间的空间依赖性和每个图像不同通道之间的相关性，以增加跨类别差异。此外，我们在 HFRP 中引入了通道维度的重建。通过通道维度和空间维度的协同重建，在支持集到查询集的重建过程中进一步增加了跨类别差异，同时在查询集到支持集的重建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了大量实验证明了我们方法的有效性和优越性。

用于少样本细粒度图像分类的混合特征协同重建网络

Hybrid Feature Collaborative Reconstruction Network for Few-Shot  Fine-Grained Image Classification

The goal of few-shot fine-grained image classification is to recognize rarely
seen fine-grained objects in the query set, given only a few samples of this
class in the support set. Previous works focus on learning discriminative image
features from a limited number of training samples for distinguishing various
fine-grained classes, but ignore one important fact that spatial alignment of
the discriminative semantic features between the query image with arbitrary
changes and the support image, is also critical for computing the semantic
similarity between each support-query pair. In this work, we propose an
object-aware long-short-range spatial alignment approach, which is composed of
a foreground object feature enhancement (FOE) module, a long-range semantic
correspondence (LSC) module and a short-range spatial manipulation (SSM)
module. The FOE is developed to weaken background disturbance and encourage
higher foreground object response. To address the problem of long-range object
feature misalignment between support-query image pairs, the LSC is proposed to
learn the transferable long-range semantic correspondence by a designed feature
similarity metric. Further, the SSM module is developed to refine the
transformed support feature after the long-range step to align short-range
misaligned features (or local details) with the query features. Extensive
experiments have been conducted on four benchmark datasets, and the results
show superior performance over most state-of-the-art methods under both 1-shot
and 5-shot classification scenarios.

该论文提出了一种物体感知的远近距离空间对齐方法，通过 FOE 模块增强前景物体响应、LSC 模块学习可转移的远距离语义对齐和 SSM 模块对查询特征进行短距离精细对齐，以实现在小样本情况下对罕见细粒度目标的检测和识别。