# Dual-Branch Multi-Scale Relation Networks with Tutorial Learning for Few-Shot Learning
## 1 Introduction
Many methods have been proposed to explore feature information at different layers to better utilize limited samples. [PERSON] et al. [14] learned a similarity metric of feature pairs at hierarchical layers using a relation network with an attention mechanism and propagated the relations to the next layer. [PERSON] et al. [15] extracted image features at different layers in the network, fused them with the transposed convolution upsampling method, and then measured the similarity to obtain the final classification results. [PERSON] et al. [16] measured features at multiple layers and introduced a voting mechanism to determine the final classification results. [PERSON] et al. [17] applied relation networks to measure the coarse-grained classification of shallow features and then measured the fine-grained classification of deep features. Similarly, some methods alleviate the problem of data scarcity by capturing multi-scale feature information. [PERSON] et al. [18] extracted features from original images at different scales and transformed them into second-order features to learn a similarity metric. [PERSON] et al. [19] performed multi-scale transformations on image features to obtain multiple scales of features and learn task-relevant feature representations at each scale, and a similarity-to-class module was employed to achieve classification. [PERSON] et al. [20] devised a multi-scale label propagation network and obtained the final label propagation scores and classification results through a weighted calculation method. [PERSON] et al. [11] resized the original image into different scales and then trained a shared relation network with a Kronecker-product module for the similarity metric. Utilizing multi-layer and multi-scale features may relieve the pressure of limited data in few-shot learning [14; 15; 16; 17; 18; 19; 20], while deeper features play a dominant role in the final classification [12; 16], but the contribution of shallow features cannot be ignored [13; 17]. Therefore, we propose a method named dual-branch multi-scale relation networks with tutoring learning for few-shot learning that effectively utilizes feature information at different layers and fully leverages the dominant role of deep feature information in classification decisions. Specifically, we learn a similarity metric between feature pairs on each deep scale in Branch 1 and on each layer in Branch 2. Recognizing the potential of the metric learning capability of the relation network, in this paper, we adopt relation networks for similarity measurement in both branches [9, 11], allowing the network to learn a suitable metric instead of being limited to a predefined distance metric. Furthermore, simply separating the relation networks to learn the similarity of feature pairs at different layers without considering the differences in feature information between layers overlooks the dominant role of deep features. Therefore, we propose using the relation network in Branch 1, which has learned more advanced feature pair similarities, to tutor the relation networks in Branch 2 to learn similarities of shallower feature pairs, resulting in improved classification performance. The contributions of this work can be summarized as follows:  
* We propose a dual-branch multi-scale metric learning method that combines deep multi-scale features and features at different layers and employee relation networks for similarity metrics. * A tutoring learning module is proposed, which involves utilizing a deep relation network in Branch 1 that has acquired advanced knowledge to tutor each shallow relation network in Branch 2 to learn similarity metrics, thereby enhancing the metric capability of relation networks. * The final classification performance obtained by our dual-branch multi-scale relation metric and tutoring learning outperforms similar methods on few-shot standard datasets.