A Multiview Metric Learning Method for Few-Shot Fine-Grained Classification

A Multiview Metric Learning Method for Few-Shot Fine-Grained Classification

Few-shot fine-grained image classification aims to solve the learning problem with few limited labeled examples. The existing methods use data augmentation to randomly transform the original examples to get new examples, and then use the new examples to train the model to improve the robustness and...

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Bibliographic Details
Main Authors: Zhuang Miao, Xun Zhao, Jiabao Wang, Bo Xu, Yang Li, Hang Li
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Access
Subjects:
Few-shot fine-grained image classification
fine-grained image classification
metric learning
date augmentation
multi-view metric
Online Access:https://ieeexplore.ieee.org/document/9775946/

Internet

https://ieeexplore.ieee.org/document/9775946/

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