Multimodal Aesthetic Evaluation Model: A Content Quality Assessment Solution Integrating Visual and Textual Modalities

This GitHub project (author: Ikannilaaa, updated on June 2, 2026) implements a multimodal aesthetic evaluation pipeline integrating visual and textual modalities. It can automatically assess the aesthetic quality of image-text combined content and is applicable to scenarios like content moderation, recommendation systems, and creative assistance. Its core value lies in breaking through the limitations of traditional single-modal evaluation, being closer to the human aesthetic judgment process, and providing a new path for automated content quality assessment.

Against the backdrop of explosive growth in digital content, automatic assessment of content aesthetic quality has become an important issue. Traditional single-modal evaluation (only images or text) struggles to capture the complete aesthetic experience, as human aesthetics are inherently a fusion of multiple senses. This project addresses this need by providing a multimodal fusion solution.

The project adopts a dual-stream encoder plus fusion layer design: the visual encoder extracts aesthetic features such as image composition, color, and texture; the text encoder extracts semantic and emotional features of the copy; the fusion layer integrates the bimodal features and outputs a comprehensive aesthetic score. This architecture preserves the specificity of each modality while capturing cross-modal correlations (e.g., image-text style matching degree).

The model can be applied in: 1. Content recommendation: As a ranking factor to enhance user experience; 2. Content moderation: Assisting in identifying low-quality content; 3. Creative assistance: Providing real-time aesthetic feedback to creators (e.g., optimizing the matching between images and copy), changing the content production process.

Currently, there are three major challenges: 1. Subjectivity: Aesthetic judgment is influenced by culture and personal preferences, requiring adaptation to diverse standards; 2. Interpretability: Black-box models are difficult to provide a basis for improvement; 3. Computational efficiency: Need to balance accuracy and real-time inference requirements. These aspects need to be optimized in a targeted manner in the future.

This project represents a practical direction for multimodal content understanding. With the growth of short videos and image-text content, the demand for automated aesthetic evaluation tools will continue to rise, which will not only change the operation mode of content platforms but also profoundly affect the creative ecosystem and processes.