# Enhanced BLIP-2 Optimization Using LoRA for Generating Dashcam Captions
## 4 Experiment  
In real-world applications such as translation systems that require both short response times and high accuracy, BLIP-2 offers a cost-effective solution. The high efficiency results from the integration of LoRA technology and the Q-Former module, which enables the model to handle complex vision-language tasks while minimizing the computational load. The use of pre-trained VLM has been widely studied [10, 11, 19], and BLIP-2 stands out owing to its frozen image encoders and low computational costs. Owing to the combination of LoRA technology and the Q-Former module, both the efficiency and performance of BLIP-2 were significantly enhanced. This model is particularly suitable for highly resource-intensive applications, and it can provide outstanding results while reducing the computational cost [20, 21]. Q-Former selectively processes only the most relevant information, while LoRA reduces the number of trainable parameters, decreasing the overall computational demand. Consequently, BLIP-2 can perform a wide range of vision-language tasks quickly and effectively, making it highly suitable for resource-limited environments [13, 22]. The rank values \(r\in\{8,16,32\}\) and scaling factors \(a\in\{16,32,64\}\) for LoRA were selected to represent a diverse range of low-rank configurations, balancing model capacity and computational efficiency. These configurations were based on empirical practices in recent literature on LoRA-based vision-language models and were chosen to reflect low, medium, and high adaptation strengths. While a full hyperparameter search was not conducted due to resource limitations, the selected settings were sufficient to reveal meaningful performance trends. Future work may incorporate a more systematic hyperparameter tuning strategy for further refinement. To validate these configurations, we assessed their impact on captioning performance. Increasing \(r\) and \(a\) led to consistent improvements, particularly in capturing structured elements like lane markings and traffic signals, enhancing contextual understanding in driving environments. To establish performance benchmarks, we evaluated multiple models, as shown in Table 2. BLIP-2 was used as a baseline without any additional fine-tuning. The LLaVA model [3] was used for comparison purposes to assess how well the BLIP-2 performed relative to an established multimodal benchmark. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **BLEU-4** & **CIDEr** & **SPICE** & **All Parameters** & **1 Epoch Time** \\ \hline BLIP-2 & 0.0117 & 0.0340 & 0.1002 & 3.8B & N/A \\ LLaVA & 0.0075 & 0.0403 & 0.0637 & 3.5B & N/A \\ Enhanced BLIP-2 (r = 8, a = 16) & 0.1318 & 0.2025 & 0.4276 & 3.83B (0.10\%) & 1.0 h \\ Enhanced BLIP-2 (r = 16, a = 64) & 0.1557 & 0.2387 & 0.4592 & 5.04B (0.13\%) & 1.2 h \\ Enhanced BLIP-2 (r = 32, a = 64) & 0.1609 & 0.2458 & 0.4642 & 7.46B (0.20\%) & 1.5 h \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of scene captioning performance across different models, including BLIP-2, LLaVA, and LoRA-enhanced BLIP-2 variants. The variable \(r\) indicates the rank of the LoRA, controlling the expressive capacity of the injected trainable layers, while \(a\) represents the scaling factor that adjusts the magnitude of LoRA updates. Although higher \(r\) and \(a\) values lead to incremental improvements in BLEU-4, CIDEr, and SPICE scores, they also result in significantly increased trainable parameters and longer training times per epoch. Considering the trade-off between performance gain and training efficiency, the configuration of \(r=8\) and \(a=16\) was selected as the most cost-effective setting in this study. B denotes the number of parameters in billions. Table 2 shows that LoRA-enhanced BLIP-2 significantly outperformed both baseline models. BLEU-4 improved 2-fold, and SPICE increased nearly 1.5-fold, indicating better fluency and semantic understanding in driving scenes.