A Multi-Modal Deep Learning Approach for Enhanced Optical Illusion Recognition

Optical illusions present intriguing challenges to human perception and cognitive processing. In this paper, we propose a novel multi-modal deep learning model designed to enhance optical illusion recognition accuracy and efficiency. By integrating visual and contextual information, along with cognitive science principles, our model aims to improve the understanding and classification of various types of optical illusions. Through extensive experimentation and validation, we demonstrate the effectiveness of our approach and discuss its potential applications in diverse domains.