The paper presents DoRA (Decomposed Orthogonal Random Adaptation) for efficient parameter tuning in large vision-language models. DoRA is designed to address the limitations of existing methods by decomposing the value and query weight matrices, allowing for more efficient adaptation of model parameters. The authors demonstrate the effectiveness of DoRA through experiments on various vision-language tasks, showing that it outperforms previous methods in terms of parameter efficiency and computational cost.

The proposed DoRA method leverages the decomposition of weight matrices to enable more efficient parameter adaptation in large vision-language models. By decomposing the value and query weight matrices, DoRA reduces the computational complexity of parameter tuning while maintaining high performance. The experiments conducted by the authors demonstrate that DoRA achieves superior parameter efficiency compared to existing methods, making it a promising approach for optimizing large-scale vision-language models.

In addition to introducing the DoRA method, the paper provides a comprehensive analysis of its performance on various vision-language tasks. The experimental results highlight the effectiveness of DoRA in improving parameter efficiency without sacrificing model performance. The authors also compare DoRA with other state-of-the-art methods, showcasing its competitive advantage in terms of computational cost and parameter optimization.