BiLSTM-TANet: an adaptive diverse scenes model with context embeddings for few-shot learning

Few-shot learning is a critical task in computer vision processing that helps reduce deep learning’s reliance on large datasets. This paper aims to establish a few-shot learning network that is adaptive to diverse scenes. A novel approach referred to as task-adapted network with bi-directional long...

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Bibliographic Details
Published in:Applied intelligence (Dordrecht, Netherlands) Netherlands), 2024-03, Vol.54 (6), p.5097-5116
Main Authors: Zhang, He, Liu, Han, Liang, Lili, Ma, Wenlu, Liu, Ding
Format: Article
Language:English
Subjects:
Ablation
Artificial Intelligence
Classification
Computer Science
Computer vision
Context
Datasets
Decision making
Deep learning
Euclidean geometry
Feature extraction
Machine learning
Machines
Manufacturing
Mathematical models
Mechanical Engineering
Optimization
Parameters
Processes
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Summary:Few-shot learning is a critical task in computer vision processing that helps reduce deep learning’s reliance on large datasets. This paper aims to establish a few-shot learning network that is adaptive to diverse scenes. A novel approach referred to as task-adapted network with bi-directional long short-term memory network (BiLSTM-TANet) is proposed in this paper. BiLSTM-TANet is an end-to-end approach based on deep metric learning and designed to use the information from finite samples as much as possible. It fuses the context embeddings and structure information of the images and adaptively adjusts the features several times during the feature extraction of task to achieve task-specific embedding and quickly adapt to different distributed tasks, improves the feature extraction performance, and strikes a balance between model stability and generality. The model employs Euclidean distance as the classifier to reduce the number of model parameters and enhance the classification performance. Experiments conducted on mini ImageNet, TieredImageNet, CUB200_2011 and CIFAR-FS datasets demonstrate the performance of the proposed BiLSTM-TANet. Furthermore, the effects of different few-shot learning parameters on the model’s performance are explored, providing a helpful reference for the future study of few-shot learning. Finally, a series of ablation studies are performed to analyze the performance of BiLSTM-TANet.
ISSN:0924-669X
1573-7497
DOI:10.1007/s10489-024-05440-y