DoRA improves LoRA by decomposing weights into magnitude and direction and updating only direction with low-rank matrices, closing much of the gap to full fine-tuning.
Evaluating Object Hallucination in Large Vision-Language Models
2 Pith papers cite this work. Polarity classification is still indexing.
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An explanatory book that supplies a clear mental map and intuition for how Vision-Language Models combine vision and language capabilities.
citing papers explorer
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DoRA: Weight-Decomposed Low-Rank Adaptation
DoRA improves LoRA by decomposing weights into magnitude and direction and updating only direction with low-rank matrices, closing much of the gap to full fine-tuning.
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From Pixels to Prompts: Vision-Language Models
An explanatory book that supplies a clear mental map and intuition for how Vision-Language Models combine vision and language capabilities.