# Dual-Branch Multi-Scale Relation Networks with Tutorial Learning for Few-Shot Learning
## 2 Related Work
### Metric-Learning-Based Methods  
This type of method typically evaluates the pairwise similarity of features between support and query samples in a feature embedding space, wherein high similarity indicates samples belonging to the same category, while low similarity suggests different categories. The general similarity measures encompass fixed metrics like Euclidean distance and cosine similarity distance or a learnable network. [PERSON] et al. [6] introduce deep neural networks into few-shot learning, where they learn the features of a pair of samples through weight-shared convolutional networks and measure similarity using Euclidean distance. [PERSON] et al. [8] encoded the support set and query set using different LSTM networks, measured feature similarity with an attention-weighted metric function and proposed a classic training mechanism for few-shot learning scenarios named the _episode_ mechanism. [PERSON] et al. [7] proposed the existence of a class prototype in each class's feature space, obtained the prototype representation by calculating the mean of same-class samples, and performed classification by measuring distances between query samples and class prototypes. [PERSON] et al. [9] suggest replacing traditional predefined nonparametric measurement methods with a learnable neural network, allowing the network to autonomously learn an appropriate similarity metric. Differing from previous approaches that rely on image-level features for similarity calculation [6; 7; 8], [PERSON] et al. [38] propose the concept of local descriptors to replace image-level feature metrics; it classifies by measuring the k-nearest neighbors of the local descriptors between the query sample and the categories. [PERSON] et al. [39] propose assigning higher weights to regions with greater similarity between images by calculating feature similarities individually for each region in comparison to the query image and subsequently combining scores across all regions for classification decisions. [PERSON] et al. [40] transform feature representation into a second-order feature representation to facilitate similarity metrics and then devise a metric strategy for multi-scale relation networks based on this foundation [18]. To address the issue of relation networks being sensitive to the spatial positions of target objects, [PERSON] et al. [10] incorporated deformable convolution into the feature extractor and combined it with a dual attention mechanism. Additionally, [PERSON] et al. [41] introduced relative position and map networks based on an attention mechanism to determine the significance of each spatial location in the image features during comparison and designed a nonlinear metric using a relative map network module. In refs [14; 15; 16] extract features of different layers to measure similarity, demonstrating that feature maps at hierarchical layers possess distinct classification characteristics and contributions, while [11; 18; 19; 20; 42] employ multi-scale feature extraction techniques to capture a broader range of feature information and boost the accuracy of classification. Simultaneously, there exist numerous methods to enhance model performance by integrating attention mechanisms [43; 44; 45; 46; 47; 48; 49] and methods that explore intra-classand inter-class relations [50; 51; 52; 53; 54] all falling under the category of metric-learning-based methods. Our method falls under the category of metric-learning-based methods and shares similarities with multi-layer methods [14; 15; 16] and multi-scale methods [18; 19; 20; 42]. We evaluate our methodology against them and several methodologies as baselines. The proposed method adopts a dual-branch structure, maximizing the utilization of feature information from different layers and scales. Additionally, learnable relation networks are employed instead of a fixed metric function for similarity measurement.