Powerful Teachers Matter: Text-Guided Multi-view Knowledge Distillation with Visual Prior Enhancement
📰 ArXiv cs.AI
Text-Guided Multi-view Knowledge Distillation (TMKD) enhances teacher knowledge quality for efficient inference
Action Steps
- Leverage dual-modality teachers, including visual and text teachers, to provide richer supervisory signals
- Enhance teacher knowledge quality using visual prior enhancement
- Apply Text-Guided Multi-view Knowledge Distillation (TMKD) for efficient inference
- Evaluate the performance of TMKD on various benchmarks and datasets
Who Needs to Know This
AI engineers and ML researchers benefit from this approach as it improves knowledge distillation, while product managers can apply it to develop more efficient AI models
Key Insight
💡 Dual-modality teachers can provide richer supervisory signals, improving knowledge distillation
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💡 Enhance teacher knowledge quality with Text-Guided Multi-view Knowledge Distillation (TMKD) for efficient AI inference
Key Takeaways
Text-Guided Multi-view Knowledge Distillation (TMKD) enhances teacher knowledge quality for efficient inference
Full Article
Title: Powerful Teachers Matter: Text-Guided Multi-view Knowledge Distillation with Visual Prior Enhancement
Abstract:
arXiv:2603.24208v1 Announce Type: cross Abstract: Knowledge distillation transfers knowledge from large teacher models to smaller students for efficient inference. While existing methods primarily focus on distillation strategies, they often overlook the importance of enhancing teacher knowledge quality. In this paper, we propose Text-guided Multi-view Knowledge Distillation (TMKD), which leverages dual-modality teachers, a visual teacher and a text teacher (CLIP), to provide richer supervisory
Abstract:
arXiv:2603.24208v1 Announce Type: cross Abstract: Knowledge distillation transfers knowledge from large teacher models to smaller students for efficient inference. While existing methods primarily focus on distillation strategies, they often overlook the importance of enhancing teacher knowledge quality. In this paper, we propose Text-guided Multi-view Knowledge Distillation (TMKD), which leverages dual-modality teachers, a visual teacher and a text teacher (CLIP), to provide richer supervisory
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