Hyperdimensional Cross-Modal Alignment of Frozen Language and Image Models for Efficient Image Captioning

Researchers introduce HDFLIM, a new framework that aligns vision and language AI models without requiring computationally expensive fine-tuning by using hyperdimensional computing to create cross-modal mappings while keeping foundation models frozen. The approach achieves comparable performance to traditional training methods while being significantly more resource-efficient.

• →HDFLIM enables cross-modal alignment between vision and language models without modifying the pretrained models themselves.
• →The framework uses hyperdimensional computing and symbolic operations instead of gradient-based optimization for image captioning.
• →Performance matches end-to-end vision-language training methods while being more computationally efficient.
• →The approach suggests foundation models may already have latent semantic compatibility without explicit multimodal training.
• →This represents a paradigm shift toward integrating frozen models through structured mappings rather than large-scale retraining.