## 5 Experimental Results and Comparison
### Quantitative Performance Comparison with Baseline Methods  
This subsection discusses our method's performance and relates the result with previous baseline methods (compared with the best score). Tables 2 and 3 report the judgment between our method and previous baseline methods. We have chosen these baseline models because all of them use the same dataset and evaluation metrics for generating video captions. Here B-4, M, C, and R-L mean BLEU@4[37], METEOR [38], CIDEr [39], and ROGUE-L [40] metrics individually. Figure 5: The example of some relevant video captioning but incorrect sentence generation and some incorrect sentence generation of our model. The generated sentences are also compared with ground truth sentences. * HATT [9]: The method introduced a two-level LSTM branch as a hierarchical order and dealt with low and high-level attention. The method semantically enriched for its fused multiple modalities for caption generation, but the period of feature extraction was very extraordinary. The method also considered the two public datasets to measure the performance. However, the model scored 52.9% in B-4 metrics. Our model, Att-BiL-SL (compared with the best score), attained a better result than HATT, with 1.9% and 13.1% increases on M and C, respectively, on the MSVD dataset. In terms of the MSR-VTT dataset, our model increased the performance by 0.2% and 13.1% in M and C metrics, respectively. * ICA-LSTM [10]: The model consisted of multiple LSTM [52] for video captioning and used dual-stage loss functionality. The method was also trained in various CNN models. Our model again accomplished a better outcome than ICA-LSTM, with 0.6% and 4% upsurges on M and C in the MSVD dataset. However, our model increased by 1% and 4% on M and C in the MSR-VTT dataset. * SF-SSAG-LSTM [12]: The method introduced augmented audio features and semantic filtration. The model generated a single-line video caption. The performance of our method upsurged 0.3% and 12% on M and C metrics in the MSVD dataset and 0.4% and 2.5% on M and C in the MSR-VTT dataset. * LSTM-GAN [13]: The model introduced a generative adversarial network and attention mechanism for video captioning. Compared to this model, our method performed better than LSTM-GAN, with performance gains of 8.3% and 5.3% on B-4 and M met \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **B-4** & **M** & **C** & **R-L** \\ \hline HATT [9] & **52.9** & 33.8 & 73.8 & - \\ ICA-LSTM [10] & 51.3 & 35.1 & 82.9 & - \\ SF-SSAG-LSTM [12] & 51.2 & 35.4 & 74.9 & - \\ LSTM-GAN [13] & 42.9 & 30.4 & - & - \\ DenseLSTM [17] & 50.4 & 32.9 & 72.6 & - \\ CAM-RNN [20] & 42.4 & 33.4 & 54.3 & 69.4 \\ VRE [23] & 51.7 & 34.3 & 86.7 & 71.9 \\ MR-HRNN [25] & 50.5 & 32.7 & 69.6 & - \\ STA-FG-RC [32] & 52.7 & 34.5 & - & - \\ Ours [Att-BiL-SL (V + 13D)] & 48.3 & 32.5 & 85.3 & 68.8 \\ Ours [Att-BiL-SL (IV3 + 13D)] & 49.1 & 33.2 & 86.4 & 70.3 \\ Ours [Att-BiL-SL (X + 13D)] & 51.2 & **35.7** & **86.9** & **72.1** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Evaluation results of our method and previous methods for the video description generation on MSVD dataset. Here, “-” indicates the metric is not considered. All the numerical terms are in percentile format. Texts with bold marks are the best scores. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **B-4** & **M** & **C** & **R-L** \\ \hline HATT [9] & 41.2 & 28.5 & 44.7 & 60.7 \\ ICA-LSTM [10] & 41.2 & 27.7 & 43.9 & - \\ SF-SSAG-LSTM [12] & 40.8 & 28.7 & 46.8 & 61.5 \\ LSTM-GAN [13] & 36.0 & 26.1 & - & - \\ DenseLSTM [17] & 38.1 & 26.6 & 42.8 & - \\ CAM-RNN [20] & 37.7 & 27.9 & 38.8 & 58.8 \\ VRE [23] & **43.2** & 28.0 & 48.3 & **62.0** \\ MR-HRNN [25] & 36.2 & 25.6 & 33.5 & - \\ STA-FG-RC [32] & 40.8 & 27.4 & - & - \\ Ours [Att-BiL-SL (V + 13D)] & 39.0 & 27.8 & 46.2 & 57.8 \\ Ours [Att-BiL-SL (IV3 + 13D)] & 40.8 & 28.3 & 48.0 & 59.8 \\ Ours [Att-BiL-SL (X + 13D)] & 41.2 & **28.7** & **49.3** & 60.4 \\ \hline \hline \end{tabular}
\end{table}