# Bi-LS-AttM: A Bidirectional LSTM and Attention Mechanism Model for Improving Image Captioning
## 4 Experiments
### Experimental Results on the Generated Image Caption  
Our image captioning model's efficacy was evaluated through comparative experiments utilizing the BLUE-N metric, with the resultant data exhibited in Table 4. The additional attention layer implemented within our model contributed significantly to its strong performance on both evaluated datasets. Substituting AlexNet with VggNet [40] resulted in substantial performance improvements across all BLUE metrics. Our model ranks predominantly within the top two positions across these metrics. While our model lags marginally behind the top-rated Hard-attention [24] model in the B-1 metric, it surpasses the performance of both the Bi-LSTM and deep Bi-LSTM models in all other assessed metrics. Figure 5 illustrates the comparison of our model with others on the METEOR and CIDEr metrics. We compared our model with the Bi-LSTM [9] and deep Bi-LSTM [8] models. As shown in the figure, the Bi-LS-AttM outperformed the leading-edge methods on the metrics. In the Flickr30K dataset, we improved the METEOR and CIDEr scores by about 8.0% and 12.5%, respectively. In the MSCOCO dataset, our model improved the \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Descriptions** & **Value** \\ \hline Emb-dim & Dimension of word embeddings. & 256 \\ Attention-dim & Dimension of Bi-LSTM attention linear layers. & 256 \\ Frcm-dim & Dimension of fast RCNN. & 128 \\ Dropout & The phenomenon of learning to adapt can be considerably reduced by training batches. & 0.5 \\ Cudnn-benchmark & Set to true only if inputs to model are fixed size; otherwise, lots of computational overhead. & True \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Model parameters. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Descriptions** & **Value** \\ \hline Lay-Num & Number of layers. & 3 \\ Time-fore & Time of data used to make the forecast. & 5 \\ Hidden-size & The size of the featured area in the hidden status. & 8 \\ Epoch & Displaying the number of forward and backward calculations that could be performed. & 120 \\ Batch-size & The amount of data transferred to the trainer. & 0.5 \\ Learning-rate & Adjustment of the network weighting rate by the loss gradient. & 0.002 \\ Optimizer & Training and parameter tuning to reduce the loss of function. & [PERSON] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Bi-LSTM attention parameters. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Descriptions** & **Value** \\ \hline Lay-Num & Number of layers. & 3 \\ Hidden-dim & Size of the featured area in the hidden status. & 8 \\ Epoch & Displaying the number of forward and backward calculations that could be performed. & 120 \\ Batch-size & The amount of data transferred to the trainer. & 64 \\ Learning-rate & Adjustment of the network weighting rate by the loss gradient. & 0.004 \\ Optimizer & Training and parameter tuning to reduce the loss function. & Adam \\ Grad-clip & Clipping to threshold, and gradient to update weights. & 5 \\ Alpha-c & Regularization parameter for attention mechanisms. & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Training parameters. METEOR and CIDEr scores by about 6.8% and 15.6%, respectively. We can also speculate that it can give better results on larger datasets. We performed a comparative evaluation of our model against other advanced models on METEOR and CIDEr metrics, as depicted in Table 5. This analysis reveals that our Bi-LS-AttM model demonstrates robust competitiveness within the MSCOCO Karpathy split dataset. Particularly, on the METEOR score, our model trails slightly behind [PERSON]'s model, potentially due to their superior parameter optimization techniques and the dataset's compatibility with their switchable novel object captioner. This calls for further investigations into novel datasets. However, our model excels in the CIDEr score. A considerable score variation is evident when comparing the performance of our model with and without the attention mechanism.