CaptionAI: An Intelligent Caption Generation System Integrating Fuzzy Logic and LLM

CaptionAI is a full-stack web application that innovatively integrates fuzzy logic and large language models. It aims to solve the problem that existing caption generation tools produce uniform results or struggle to capture users' vague preferences, enabling the generation of context-aware captions for images and videos based on users' personalized vague needs. The system uses fuzzy logic to handle the uncertain preferences in human language and combines LLM to generate high-quality text, demonstrating an important direction for AI to understand human needs that are difficult to quantify.

In the era of social media, the demand for adding captions to images and videos has become daily. However, existing tools face a dilemma: fixed templates lead to uniform results, while pure AI generation struggles to capture personalized expressions. What's more challenging is that humans often use vague language to describe caption styles (e.g., "not too formal" or "a bit humorous"), and these hard-to-quantify preferences pose challenges to automated systems.

The uniqueness of CaptionAI lies in the deep integration of fuzzy logic and LLM:

1. Fuzzy Logic Layer: Converts users' natural language preferences into computable logical rules through membership functions, establishing continuous spectrums in dimensions such as "formal-casual" and "brief-detailed" to handle the uncertainty in human language;
2. LLM Layer: Combines visual encoders to understand image content and grasp platform tone, integrates style parameters guided by fuzzy logic, and generates fluent, context-appropriate caption text.

CaptionAI supports multimodal input processing:

• Static image analysis: Identifies scenes, objects, emotional atmosphere, and visual focus;
• Video content understanding: Extracts key frames, action sequences, and narrative rhythm;
• Context awareness: Adjusts language style based on the characteristics of the publishing platform;
• Personalized adaptation: Learns users' historical preferences and continuously optimizes generation strategies.

Inferred tech stack from the architecture description:

• Frontend: Modern web interface supporting image upload and preference input;
• Visual understanding module: Likely based on CLIP or similar vision-language pre-trained models;
• Fuzzy inference engine: Custom fuzzy rule base and inference engine;
• Text generation module: Calls GPT series or open-source LLM APIs;
• Preference learning mechanism: Optimizes membership function parameters through a closed loop of user feedback.

CaptionAI's application scenarios include:

• Social media operators: Batch generation of brand content with a unified style;
• Ordinary users: Solves the pain point of "not knowing what copy to write for Moments";
• Accessibility field: Provides a more intelligent image description tool for visually impaired people. Its value also lies in demonstrating the direction of AI design: enabling machines to better understand and execute human preferences that are difficult to express precisely, rather than replacing human judgment.

As an early-stage project, CaptionAI currently has limited stars and community activity, and is in the proof-of-concept phase. Future directions may include:

• Supporting caption generation in more languages;
• Introducing more refined emotion and tone control;
• Integrating with mainstream social media platform APIs;
• Open-sourcing the core fuzzy logic rule base for community contributions.