Large Language Model Teaches Visual Students: Cross-Modality Transfer of Fine-Grained Conceptual Knowledge
arXiv:2606.27527v1 Announce Type: cross Abstract: Large Language Models (LLMs) possess broad conceptual knowledge acquired through large-scale text pretraining, yet their potential to supervise models in other modalities remains underexplored. In this work, we propose LaViD--Language-to-Visual Knowledge Distillation--a simple and effective framework for transferring high-level semantic knowledge from a language-only teacher to a vision-only student model. Instead of relying on paired multimodal data, LaViD elicits conceptual signals from an LLM by prompting it to generate multiple-choice quest
The rapid advancement and broad conceptual knowledge of LLMs have recently made such cross-modal knowledge transfer approaches feasible and highly effective.
This research outlines a method to leverage the deep understanding of LLMs to train visual models without relying on expensive paired multimodal datasets, significantly accelerating AI development.
The ability to transfer high-level semantic knowledge from language models to vision models will reduce data dependency and potentially democratize advanced AI capabilities.
- · AI developers
- · Computer vision researchers
- · Companies with large language models
- · Sectors requiring sophisticated visual AI
- · Companies reliant on bespoke, large-scale multimodal data collection
Vision models will become more sophisticated and context-aware, particularly in fine-grained tasks.
Development costs and time for new visual AI applications will significantly decrease, leading to a proliferation of AI-powered systems.
This could accelerate the integration of AI agents across various industries, as visual perception becomes more robust and easier to develop.
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Read at arXiv cs.LG