Integrating Human Vision Perception in Vision Transformers for Classifying Waste Items

TL;DR

This paper introduces a novel waste classification method inspired by human vision, specifically simulating nystagmus through differential blurring, which improves Vision Transformer's accuracy by 2%.

Contribution

It presents a new approach that integrates human visual perception phenomena into Vision Transformers to enhance waste classification accuracy.

Findings

Outperforms standard Vision Transformer by 2% in waste classification.

Simulates nystagmus through differential blurring to mimic human vision.

Potential for broader application in global issues.

Abstract

In this paper, we propose an novel methodology aimed at simulating the learning phenomenon of nystagmus through the application of differential blurring on datasets. Nystagmus is a biological phenomenon that influences human vision throughout life, notably by diminishing head shake from infancy to adulthood. Leveraging this concept, we address the issue of waste classification, a pressing global concern. The proposed framework comprises two modules, with the second module closely resembling the original Vision Transformer, a state-of-the-art model model in classification tasks. The primary motivation behind our approach is to enhance the model's precision and adaptability, mirroring the real-world conditions that the human visual system undergoes. This novel methodology surpasses the standard Vision Transformer model in waste classification tasks, exhibiting an improvement with a margin of 2%. This improvement underscores the potential of our methodology in improving model precision by drawing inspiration from human vision perception. Further research in the proposed methodology could yield greater performance results, and can be extrapolated to other global issues.