arXiv:2606.23897v1 Announce Type: cross Abstract: Prompt distillation compresses large vision-language models (VLMs) such as CLIP into lightweight student models by matching teacher predictions on unlabeled domain images. PromptKD (CVPR 2024) established this paradigm with a single PromptSRC-finetuned ViT-L/14 teacher and a ViT-B/16 student. We propose TheProfessor, a multi-teacher extension that distills from a fixed two-teacher ensemble: a domain-finetuned PromptSRC ViT-L/14 teacher and a zero-shot EVA-CLIP-L/14 teacher whose logits are pre-computed per dataset. We evaluate single-teacher Pr
The continuous drive for more efficient and performant AI models necessitates ongoing research into distillation techniques to reduce computational overhead.
This development allows for the deployment of more lightweight yet capable vision-language models, expanding their applicability in resource-constrained environments and accelerating model development cycles.
The ability to distill knowledge from multiple 'teacher' models rather than a single one can lead to student models with improved performance and robustness, making advanced AI capabilities more accessible.
- · AI researchers
- · Edge computing providers
- · Hardware developers
- · Companies reliant solely on large, complex models
- · Resource-intensive AI deployment strategies
More efficient vision-language models become available for various applications.
Reduced computational costs for deploying AI models could democratize access to advanced AI functionalities.
The proliferation of lightweight, multi-teacher distilled models could accelerate innovation in new AI-powered products and services.
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Read at arXiv cs.AI