HCMC: A Humor-Aware Cross-Modal Captioning System Designed for Cartoon Images

HCMC (Hybrid Cross-Modal Captioner) is an advanced multimodal AI system specifically designed to generate humorous and contextually relevant captions for cartoon images. Unlike traditional image captioning models, HCMC can understand abstract visuals, satire, and social contexts in cartoons.

Image Captioning is a classic problem at the intersection of computer vision and natural language processing. However, most existing models are trained on natural images and perform poorly when dealing with cartoon images. This is because cartoon images have a unique visual language—exaggerated abstract expressions, satirical social commentary, and humorous elements that require cultural context to understand.

The HCMC (Hybrid Cross-Modal Captioner) project was created to address this challenge; it is a multimodal AI system specifically designed for cartoon images, capable of understanding and generating humorous captions that match the cartoon content.

Compared to traditional captioning models, HCMC has the following unique capabilities:

Cartoon artists often use exaggerated proportions, simplified lines, and symbolic visual elements to express complex concepts. HCMC captures these abstract features through a specialized visual encoder.

Many cartoon works contain satire and commentary on social phenomena. HCMC can identify these subtle social context clues and reflect them in the generated captions.

Humor often arises from the contrast between expectation and reality. HCMC's humor scoring module is specifically trained to identify this incongruity and generate witty captions.

HCMC uses a modular hybrid architecture that integrates multiple advanced AI components:

As a visual feature extractor, ViT converts cartoon images into high-dimensional visual representations, capturing key visual elements and composition information in the images.