Few-shot learning based on hierarchical classification via multi-granularity relation networks

Su, Yuling; Zhao, Hong; Lin, Yaojin

doi:10.1016/j.ijar.2021.12.013

Cited by 11 publications

(2 citation statements)

References 19 publications

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“…Huang J. et al ( 2022 ) present a series of local-level approaches to improve the few-shot image classification by preventing the discriminative location bias and information loss in local details, which enhance prototypical few-shot learning. Su et al ( 2022 ) propose a few-shot hierarchical classification model using multi-granularity relation networks (HMRN) that takes the inner-class similarity and inter-class relationship into account. This model can improve the ability of classification by reducing the inner-class distance and increasing the inter-class distance.…”

Section: Metric-based Few-shot Image Classificationmentioning

confidence: 99%

Learning with few samples in deep learning for image classification, a mini-review

Zhang

Liu

2023

Front. Comput. Neurosci.

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Deep learning has achieved enormous success in various computer tasks. The excellent performance depends heavily on adequate training datasets, however, it is difficult to obtain abundant samples in practical applications. Few-shot learning is proposed to address the data limitation problem in the training process, which can perform rapid learning with few samples by utilizing prior knowledge. In this paper, we focus on few-shot classification to conduct a survey about the recent methods. First, we elaborate on the definition of the few-shot classification problem. Then we propose a newly organized taxonomy, discuss the application scenarios in which each method is effective, and compare the pros and cons of different methods. We classify few-shot image classification methods from four perspectives: (i) Data augmentation, which contains sample-level and task-level data augmentation. (ii) Metric-based method, which analyzes both feature embedding and metric function. (iii) Optimization method, which is compared from the aspects of self-learning and mutual learning. (iv) Model-based method, which is discussed from the perspectives of memory-based, rapid adaptation and multi-task learning. Finally, we conduct the conclusion and prospect of this paper.

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Section: Metric-based Few-shot Image Classificationmentioning

confidence: 99%

Learning with few samples in deep learning for image classification, a mini-review

Zhang

Liu

2023

Front. Comput. Neurosci.

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“…But in the current production background, especially when the samples are not enough, they can not show good recognition effect. Therefore, this research proposed a method to identify the pests and diseases of crop leaves from the perspective of few-shot learning (Su et al, 2022).…”

Section: Related Workmentioning

confidence: 99%

Research on Identification of Crop Leaf Pests and Diseases Based on Few-Shot Learning

Wang,

Zhang,

Han

et al. 2023

Eng. Agríc.

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The yield of crops has a significant impact on economic and social development. It is significant to ensure the healthy growth of crops. Leaves can represent the growth of crops. Crop health can be monitored by analyzing a sufficient number of leaf images. But advanced farming techniques make leaves less susceptible to pests and diseases. Therefore, it is difficult to collect enough leaves with pests and diseases for image analysis. To solve this problem, this research proposed a method based on few-shot learning to identify crop leaf images and judge crop health status. The main structure of the method is a siamese network. The structure of its two subnetworks is the convolution neural network with an attention module. Each subnetwork outputs a feature vector. Measuring the distance of two feature vectors in the feature space, the similarity is calculated. Then the categories of leaf pests and diseases are judged. The experiments in this research were carried out on apple and potato leaves. The accuracy of identifying their pests and diseases reached 98.03% and 97.34% respectively. The experiment proved that when the sample size is small. The method proposed can effectively identify crop leaf pests and diseases.

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Exploring the Interpretability of Sequential Predictions Through Rationale Model

Al Saidat,

Salloum,

Shaalan

2024

Studies in Big Data

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Few-shot learning based on hierarchical classification via multi-granularity relation networks

Cited by 11 publications

References 19 publications

Learning with few samples in deep learning for image classification, a mini-review

Learning with few samples in deep learning for image classification, a mini-review

Research on Identification of Crop Leaf Pests and Diseases Based on Few-Shot Learning

Exploring the Interpretability of Sequential Predictions Through Rationale Model

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