# Dual-Branch Multi-Scale Relation Networks with Tutorial Learning for Few-Shot Learning
## 1 Introduction  
Due to the availability of a large amount of labeled data, deep learning yields impressive results in the field of computer vision [1, 2, 3, 4]. However, training deep learning models often relies on a significant amount of labeled data. When faced with limited data and sparse samples, the model is prone to overfitting, resulting in poor generalization ability. This situation does exist in practical problems, such as image data of rare species and medical images. In contrast, humans possess powerful learning and cognitive abilities that allow them to establish knowledge of new concepts through learning only a few examples or even a single concept [5]. Therefore, inspired by human cognitive abilities, determining how to enable machines to establish effective cognition and generalize to new categories through training with only a few samples is one of the significant research issues in the field of deep learning, known as few-shot learning. Few-shot learning refers to the process of training a model with limited available examples, enabling the model to generalize to new categories and establish robust cognition, simulating the human process of connecting prior knowledge with novel concepts. In the context of few-shot classification problems, a classification model is trained using limited data and then tasked with classifying new categories that were not encountered during training, thus necessitating strong generalization capabilities. To address such challenges,numerous exemplary approaches have been proposed, which can be roughly categorized into three groups: meta-learning-based, data-augmentation-based, and metric-learning-based methods. Revolving around the meta-learning paradigm, approaches based on meta-learning train a meta-learner across tasks, focusing on training at the task level rather than the sample level. Its objective is to update the meta-learner with task-agnostic knowledge acquired to obtain optimal parameters that can adapt to novel tasks. On the other hand, data-augmentation-based methods attempt to mitigate the problem of limited sample data by generating new data from the existing samples, utilizing additional information, or enhancing sample representations. Meanwhile, metric-learning-based methods focus on constructing an embedding space where robust features are extracted and learning an informative similarity metric in the embedding space. Samples of the same class have a higher similarity metric, while it is lower for samples of different classes. These methods have achieved remarkable success, among which the metric-learning-based methods are the simplest and most effective. In [6; 7; 8], they first map the images into a feature space and then use predefined distance functions such as Euclidean distance and cosine distance to compute the similarity between query and support image in pairs for classification. [PERSON] et al. [9] proposed a relation network to measure the similarity between samples, which can obtain a suitable distance metric instead of a fixed distance metric through learning. However, the relation network is known sensitive to spatial location, which may result in the misalignment of the compared objects within a certain region. To alleviate this issue, [PERSON] et al. [10] adopted the deformable convolution combined with a dual correlation attention mechanism. [PERSON] et al. [11] proposed a Kronecker-product module that maps and fuses positionwise correlations between the query and support feature pairs. However, these methods still fail to fully exploit the limited data and ignore a wealth of valuable information, such as the image features at different layers in deep learning models. These features contain distinct image information that is useful for classification. Deeper layers can capture more comprehensive semantic information about the object [12], while shallow layers can learn texture features such as lines and contours [13], which also contribute to enhancing recognition capabilities.