A multimodal adaptive deep network for emotion recognition in artistic images

Key Points

• This research aims to develop an adaptive deep network for accurately recognizing emotions conveyed in artistic images.
• Developed a multimodal deep learning framework for emotion analysis.
• Tested the framework on a dataset of various artistic images for emotion classification.
• Analyzed performance through accuracy metrics and comparative benchmarks.
• Achieved high accuracy rates in emotion recognition from artistic images, surpassing traditional methods.
• Demonstrated the network's adaptability to different artistic styles and emotional contexts.

Abstract