# Video Description Model Based on Temporal-Spatial and Channel Multi-Attention Mechanisms
## 4 Experiment
### Experimental Result  
#### Analysis of Different Attention Combinations  
The multi-attention video description model we proposed in this paper is shown in Figure 3 and contains temporal, spatial, and channel attention mechanisms represented by T (temporal), S (spatial), and C (channel), respectively. We can combine the three attention mechanisms to build six basic models: T-S-C, T-C-S, S-T-C, S-C-T, C-T-S, and C-S-T. Besides, because the information in the time domain and the space domain are often correlated in a video, we consider the space and the time as a whole so that we build two models: ST-C, and C-ST. Therefore, our model has eight different combinations in total. [PERSON] et al. [10] proposed the Base Model. The experimental results on MSVD are shown in Table 1. The experimental result is the statistical value of multiple experiments. All the test results were obtained by a simple greedy search method.  
The results show that the S-C-T model has the best results on CIDEr, METEOR and ROUGE_L evaluation indexes, and the ST-C model has the best result on BLEU-4 evaluation index. Therefore, the S-C-T model was the best one in the first six models while the C-S-T model performed worst. This difference indicated that the model focused more on certain spatial region features of video frames in the video description model, and improving the effectiveness of the model by obtaining effective spatial features. Similarly, when we considered the space and the time as a whole, the results of ST-C performed well on certain performance indexes. According to the results, if the model initially focused on the channel attention, then the results were relatively poor among all the models. Because the channel attention focused on certain semantics of the local features in the video, which indicated that focusing on the global feature initially, the local features helped improve the performance of the model.  
Table 2 shows the results of the S-C-T model on Greedy Search and Beam Search. For the Beam Search algorithm, the test results of K from 2 to 10 are shown in this table. The experimental result is the statistical value of multiple experiments It can be seen that the Beam Search algorithm has a significant improvement on BLEU-4, CIDEr, and METEOR, and a small improvement on ROUGE_L. This shows that the Beam Search algorithm has better results. Because compared to the Greedy Search algorithm, the Beam Search algorithm has more search paths and explores more possible sentence sequences. However, as the K increases, the calculation time of Beam Search algorithm increases, and meanwhile, the result tends to be stable. Therefore, it is necessary to select an appropriate K to achieve a balance in time and performance, and usually the K is selected to be 6 or less. As Table 2, when K = 6, the S-C-T model can obtain the best results in this paper.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Structure** & **BLEU-4** & **CIDEr** & **METEOR** & **ROUGE_L** \\ \hline Base Model & 41.2 & 64.8 & 31.4 & 67.3 \\ T-S-C & **44.9** & 76.6 & 32.1 & 68.9 \\ T-C-S & 44.3 & 74.5 & 32.2 & 68.4 \\ S-T-C & 43.5 & 74.9 & 32.2 & 68.1 \\ S-C-T & 44.6 & **78.0** & **32.6** & **68.9** \\ C-T-S & 44.4 & 76.1 & 32.4 & 68.4 \\ C-S-T & 43.5 & 73.1 & 32.1 & 68.0 \\ C-ST & 43.6 & 72.6 & 32.1 & 67.9 \\ ST-C & **44.9** & 75.5 & 32.4 & 68.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Test results of different combinations on Microsoft Video Description (MSVD).