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Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing

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Insights into few shot learning approaches for image scene classification.

Mohamed Soudy¹, Yasmine Afify², Nagwa Badr²

¹Bioinformatics Program, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt.

Peerj. Computer Science

|October 7, 2021

Summary

This study introduces novel few-shot learning models for scene classification, addressing data limitations in computer vision. The proposed models and datasets demonstrate superior accuracy compared to existing benchmarks.

Keywords:

Few shot learning Places Reptile Scene classification Sun397

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Area of Science:

Computer Vision
Machine Learning
Artificial Intelligence

Background:

Image understanding and scene classification are vital in computer vision.
Current machine learning models excel but are data-dependent and require extensive training data.
Few-shot learning addresses data limitations but is underexplored in scene classification.

Purpose of the Study:

To introduce novel models for few-shot scene classification.
To develop new datasets (MiniSun, MiniPlaces) for evaluating few-shot scene classification.
To enhance the performance of models with limited training data.

Main Methods:

Development of two novel models specifically designed for few-shot learning in scene classification.
Introduction of two new datasets, MiniSun and MiniPlaces, tailored for few-shot image scene classification.
Empirical evaluation of the proposed models against benchmark approaches.

Main Results:

The proposed few-shot learning models achieve superior classification accuracy.
Experimental results validate the effectiveness of the new models on the introduced datasets.
Outperformance of benchmark methods in few-shot scene classification tasks.

Conclusions:

The developed models offer a promising solution for few-shot scene classification.
The new datasets facilitate further research in this specialized area of computer vision.
This work advances the capabilities of machine learning models in classifying scenes with limited data.