# Video Description Model Based on Temporal-Spatial and Channel Multi-Attention Mechanisms
## 4 Experiment
### Experimental Result
#### 4.3.2 Comparison with Methods in Other Papers  
To verify the effectiveness of the model, we compared our methods with other methods in existing relevant articles, including basic Seq-Seq (S2 VT) [11], joint embedded model (LSTM-E) [31], temporal attention model (TA) [13], hierarchical model (HRNE-SA) [32], paragraph description model (P-RNN) [33], task-driven dynamic model (TDDF) [34], multi-level attention model based RNN (MAM-RNN) [35], LSTM-GAN model [7], video captioning model with tube features (VCTF) [36], VD-SVOs model [37], and spatial-temporal attention mechanism (STAT) [38]. Among them, the joint embedded model, HRNE-SA, and P-RNN used the average pooling, spatial attention, and temporal-spatial attention, respectively. To compare with other models effectively, this model utilized the Beam Search algorithm, where K is 6. Table 3 shows that our S-C-T model was the best on CIDEr and METEOR, and consistent with the best method TDDF on the ROUGE-L. Compared with STAT, although our S-C-T model is weaker than STAT on BLEU-4, it is much stronger than STAT on CIDEr index, and slightly better on METEOR. However, STAT uses image features, motion features and local features, while we only use image features, which makes our S-C-T model easy to implement. Furthermore, when STAT only employ image features (STAT_GlobFeat), our model is superior to STAT on CIDEr and METEOR, and almost consistent in BLEU-4. One prominent progress in our experiment was the significant improvement of CIDEr compared with others. Meanwhile, different from other indexes, CIDEr is exclusively used in image video description index and it refers to the punctuation of words, the accuracy of word order, the accuracy of semantic and content descriptions, and fluency synthetically. Therefore, this experiment verified the effectiveness of our multi-attention model on taking advantage of the visual features and solving the problem of consistency between visual features and the semantic description. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test Method** & **BLEU-4** & **CIDEr** & **METEOR** & **ROUGE\_L** \\ \hline Greedy Search & 44.6 & 78.0 & 32.6 & 68.9 \\ Beam Search (2) & 48.3 & 80.2 & 33.2 & 69.6 \\ Beam Search (3) & 48.4 & 80.3 & 32.8 & 68.9 \\ Beam Search (4) & 47.8 & 80.0 & 33.0 & 68.7 \\ Beam Search (5) & 48.4 & 80.4 & 33.1 & 68.6 \\ Beam Search (6) & **48.8** & **82.0** & **33.4** & **69.7** \\ Beam Search (7) & **48.8** & 79.8 & 33.3 & 68.5 \\ Beam Search (8) & 48.3 & 78.8 & 33.2 & 68.3 \\ Beam Search (9) & 48.3 & 78.3 & 33.1 & 68.2 \\ Beam Search (10) & 48.5 & 78.2 & 33.2 & 68.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Test results of the spatial-channel-temporal (S-C-T) model on Greedy Search and Beam Search. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **BLEU-4** & **CIDEr** & **METEOR** & **ROUGE-L** \\ \hline S2 VT [11] & - & - & 29.8 & - \\ LSTM-E(VGG+C3D) [31] & 45.3 & - & 31.0 & - \\ TA [13] & - & 51.7 & 29.6 & - \\ HRNE-SA [32] & 43.8 & - & 33.1 & - \\ P-RNN-VGG [33] & 44.3 & 62.1 & 31.1 & - \\ P-RNN-C3D [33] & 47.4 & 53.6 & 30.3 & - \\ TDDF(VGG+3D) [34] & 45.8 & 73.0 & 33.3 & **69.7** \\ MAM-RNN [35] & 41.3 & 53.9 & 32.2 & 68.8 \\ LSTM-GAN [7] & 42.9 & - & 30.4 & - \\ VCTF [36] & 43.8 & 52.2 & 32.6 & 69.3 \\ VD-SVOs [37] & 40.5 & 58.4 & 30.2 & - \\ STAT\_GlobFeat [38] & 48.9 & 67.1 & 32.6 & - \\ STAT [38] & **52.0** & 73.8 & 33.3 & - \\ Our S-C-T (K = 6) & 48.8 & **82.0** & **33.4** & **69.7** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of different models on MSVD, where (-) indicates unknown scores. To analyze the model on a larger dataset, we evaluated the model on the MSR-VTT [25] dataset. The result is shown in Table 4. Among the models in Table 3, only TDDF, LSTM-GAN and STAT were tested on MSR-VTT, thus our paper only compares with these models. As Table 4, our model has an improvement on BLEU-4 and METEOR, and has a little improvement on ROUGE-L. However, the results of CIDEr show significant differences on the MSVD and MSR-VTT datasets.