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Powerful Teachers Matter: Text-Guided Multi-view Knowledge Distillation with Visual Prior Enhancement
arXiv – CS AI|Xin Zhang, Jianyang Xu, Hao Peng, Dongjing Wang, Jingyuan Zheng, Yu Li, Yuyu Yin, Hongbo Wang|
🤖AI Summary
Researchers propose Text-guided Multi-view Knowledge Distillation (TMKD), a new method that uses dual-modality teachers (visual and text) to improve knowledge transfer from large AI models to smaller ones. The approach enhances visual teachers with multi-view inputs and incorporates CLIP text guidance, achieving up to 4.49% performance improvements across five benchmarks.
Key Takeaways
- →TMKD introduces dual-modality teachers combining visual and text (CLIP) models for enhanced knowledge distillation.
- →The method incorporates visual priors like edge and high-frequency features into multi-view inputs for better teacher quality.
- →Vision-language contrastive regularization strengthens semantic knowledge in student models.
- →Experimental results show consistent improvements up to 4.49% across five different benchmarks.
- →The approach addresses the often-overlooked aspect of enhancing teacher model quality rather than just distillation strategies.
#knowledge-distillation#machine-learning#clip#multi-modal#computer-vision#model-compression#ai-research#deep-learning
Read Original →via arXiv – CS AI
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