Widthwise pruning of LVLM language backbones combined with supervised finetuning and hidden-state distillation recovers over 95% performance using just 5% of data across 3B-7B models.
In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp
2 Pith papers cite this work. Polarity classification is still indexing.
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RATNet applies analogical reasoning via a cyclic pre-training strategy to outperform prior foundation models in GI endoscopy diagnosis across diagnosis, few-shot, zero-shot, robustness, adaptation, and federated scenarios.
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Structural Pruning of Large Vision Language Models: A Comprehensive Study on Pruning Dynamics, Recovery, and Data Efficiency
Widthwise pruning of LVLM language backbones combined with supervised finetuning and hidden-state distillation recovers over 95% performance using just 5% of data across 3B-7B models.
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Analogical Reasoning as a Doctor: A Foundation Model for Gastrointestinal Endoscopy Diagnosis
RATNet applies analogical reasoning via a cyclic pre-training strategy to outperform prior foundation models in GI endoscopy diagnosis across diagnosis, few-shot, zero-shot, robustness, adaptation, and federated scenarios.