Perceptual Diversity in Multilingual Vision-Language Models: A New Exploration of Multimodal Redescription Framework

The AACL-IJCNLP 2025 research project focuses on the problem of perceptual diversity in multilingual vision-language models (VLMs) — systematic differences exist in how speakers of different languages describe the same visual content, which poses challenges to model fairness and accuracy. The project proposes a multimodal redescription framework, aiming to enhance the cross-lingual capabilities of VLMs by introducing an intermediate redescription step, balancing technical improvements with cultural respect.

Human visual perception is influenced by language and cultural backgrounds. When native speakers of different languages observe the same image, there are differences in attention to details, assignment of importance, and conceptual frameworks (e.g., distinctions in color and spatial relationships). Traditional VLMs are trained with single-language description pairs, assuming that the semantic mapping of visual content is the same across languages, leading to reduced generation quality and even reinforcing cultural biases.

The core of the framework is introducing a redescription step before translation. Key components include: 1. Perceptual diversity modeling: Analyze large-scale multilingual image description datasets to identify differences in lexical preferences, description granularity, culture-specific concepts, etc.; 2. Conditional redescription generation: Generate new descriptions that align with the perceptual habits of the target language based on the image and target language identifier; 3. Multi-stage training: First learn general vision-language alignment, then model language-specific perception, and finally perform end-to-end fine-tuning.

Three major challenges are faced at the technical level: 1. Encoding language-specific perceptual preferences while maintaining general capabilities: Adopt an adapter mechanism, adding lightweight perceptual adaptation modules to the base VLM; 2. Training data quality: Crowdsource native speaker description data and design quality control mechanisms; 3. Evaluation metrics: Combine human evaluation with automatic metrics (e.g., BLEU, CIDEr) to capture subtle differences in perceptual diversity.

The research significance goes beyond technical improvements and touches on multilingual AI fairness and inclusivity — the model can generate descriptions that align with different linguistic and cultural habits, both improving accuracy and respecting users' linguistic identities. Application scenarios include multilingual visual search, cross-cultural content recommendation, and assisting visually impaired individuals, which is a key differentiator for AI products serving global users.

Current limitations: Only focuses on image description tasks; complex visual reasoning or multi-turn dialogue scenarios have not been explored; the cognitive science mechanism of language-perception interaction remains controversial. Future directions: Expand to low-resource languages; explore the performance of perceptual diversity in other vision-language tasks; integrate perceptual modeling with other capabilities of large multimodal models.

The multimodal redescription framework provides a new perspective for the multilingualization of VLMs, reminding us that technological globalization is not just about language translation, but also about respect and understanding of cultural and perceptual ways. As AI increasingly intervenes in human visual experiences, attention to diversity will become more important.