# MDPN: Multilevel Distribution Propagation Network for Few-Shot Image Classification
###### Abstract  
Due to a shortage of labeled examples, few-shot image classification frequently experiences noise interference and insufficient feature extraction. In this paper, we present a two-stage framework based on the distribution propagation graph neural network (DPGN) called the multilevel distribution propagation network (MDPN). An instance-segmentation-based object localization (ISOL) module and a graph-based multilevel distribution propagation (GMDP) module are both included in the MDPN. To create a clear and full object zone, the ISOL module generates a mask that eliminates background and pseudo-object noises. The GMDP module enriches the level of features. We carried out comprehensive experiments on the few-shot dataset CUB-200-2011 to show the usefulness of MDPN. The results demonstrate that MDPN indeed outperforms DPGN in terms of few-shot image classification accuracy. Under 5-way 1-shot and 5-way 5-shot settings, the classification accuracy of MDPN exceeds the baseline by 8.17% and 1.24%, respectively. MDPN also outperforms the majority of the existing few-shot classification methods in the same setting. deep learning; few-shot classification; graph neural network +
Footnote †: journal: Applied  
0000-0002-1313-extraction module to get different levels of features by stacking graph neural networks (GNNs) in sequence, with the goal of improving the feature extraction capability of the lightweight backbone. The impact of noise on prediction accuracy will be particularly clear when the backbone of FSL has limited feature extraction capabilities. The dataset CUB 200-2011 that we utilize in particular for our research is a dataset of birds in a natural scene. The backgrounds of the birds in CUB 200-2011 primarily consist of the sky, branches, flowers, stones, and water, as seen in Figure 1. These backgrounds are intricate, and occasionally the birds even blend in with them. Eliminating pseudo-object interference and precisely extracting the target object from the image in FSL is also a big challenge for the model. As shown in Figure 2, several types of bird images contain a particular sort of pseudo-object, such as human hands, tree stumps, cages, water dispensers, oranges, etc. When a specific type of pseudo-object is included in the support set and query set of an episode (support set, query set, and episode are all words used in the FSL domain). An episode means a task; the support set equals the training set, and the query set equals the testing set. The model may mistakenly treat the pseudo-object as the target object and misclassify the two photos of distinct types of birds as belonging to the same category. In this study, we present an instance-segmentation-based object localization module that creates a new picture containing only the target object by precisely segmenting along the object's edge in order to exactly remove the interference of backdrop and pseudo-object. Figure 1: Complex backgrounds in CUB-200-2011. In this paper, we present an MPDN for few-shot image classification that combines an instance-segmentation-based object localization (ISOL) module with a graph-based multilevel distribution propagation (GMDP) module to address these issues. The instance segmentation adopted by the ISOL module is based on prior knowledge. Using the previously known segmentation of the images, ISOL divides the raw images into segments based on the outer contour of the target object and masks the non-object portions. The final outputs of ISOL are the regions included in each object's minimum bounding rectangle (MBR). The GMDP module, which consists of three graph networks concatenated in series, is used to post-process the features. The outcomes of GPDN are three layers of distributions with increasing abstraction. We then use these three distributions to update the original features that were supplied to the GMDP module, and we send the revised features back to the module to be used in the following iteration.