## 5 Experimental Results and Comparison  
For our Att-BiL-SL model, we considered the most popular freely available MSVD [41] and MSR-VTT [35] datasets and evaluated the generated captions with automatic machine translator evaluation metrics. BLEU [37], METEOR [38], ROUGE-L [40], and CIDEr [39] are very communal assessment measurements on image and video depictions. The foremost three were primarily planned to assess machine interpretation at the soonest, and CIDEr was proposed to assess the image portrayal with adequate reference sentences. The METEOR could catch the semantic viewpoint since it recognizes all potential matches by extricating precise matches, stem matches, reword matches, and equivalent matches by utilizing the WordNet database and computing sentence level likeness scores per matching loads. To quantitatively assess the presentation of our methodology, we received all the metrics in light of their powerful execution. Here, Table 1 reports the performance measurements of our proposed model.  
Table 1 shows the various scores that we evaluated by applying various evaluation metrics. The evaluation metrics are BLUE@4 (B-4), METEOR (M), CIDEr, and ROGUE-L (R-L). The proposed model considered the two most popular datasets, such as MSVD and MSR-VTT, that are publicly available. According to the MSVD dataset, the model with Xception + Inflated 3D scored **51.2%** in B-4, **35.7%** in M, **86.9%** in C, and **72.1%** in R-L, which is the best possible result of our entire proposed model. In the MSR-VTT dataset, our model with Xception + Inflated 3D scored **41.2%** in B-4, **28.7%** in M, **49.3%** in C, and **60.4%** in R-L. We found the best results from the Xception + Inflated 3D attention-based Bi-LSTM and Sequential LSTM CNN model in both datasets. The pictorial view of the outcomes of our proposed method is illustrated in Figures 4 and 5.  
Our proposed model shows promising results in all automatic machine translator evaluation metrics. The model trained in three different CNN models for feature extraction and all of them performed very significantly. All the CNN models passed through the same attention-based Bi-LSTM and Sequential LSTM (Att-BiL-SL) encoder-decoder model. Our Att-BiL-SL with VGG net [42] beat many previous baseline methods in two popular datasets. The InceptionV3 net [47] also outperformed in most cases, but the Xception net  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**MSVD**} & \multicolumn{4}{c}{**MSR-VTT**} \\ \cline{2-9}  & **B-4** & **M** & **C** & **R-L** & **B-4** & **M** & **C** & **R-L** \\ \hline Att-BiL-SL (V + 13D) & 48.3 & 32.5 & 85.3 & 68.8 & 39.0 & 27.8 & 46.2 & 57.8 \\ Att-BiL-SL (IV3 + 13D) & 49.1 & 33.2 & 86.4 & 70.3 & 40.8 & 28.3 & 48.0 & 59.8 \\ Att-BiL-SL (X + 13D) & **51.2** & **35.7** & **86.9** & **72.1** & **41.2** & **28.7** & **49.3** & **60.4** \\ \hline \hline \end{tabular}
\end{table}
Table 1: The performance evaluation of our model in MSVD and MSR-VTT datasets. Here, the V, IV3, X, and I3D denote the VGG-16 net, InceptionV3 net, Xception net, and Inflated 3D network, respectively. Text with bold marks denote the best results in evaluation metrics (percentile score).