## 4 Experiments
### Comparison with state of the art
#### 4.3.2 Qualitative Analysis  
In this part, we show some test examples of what our model is paying attention to in the videos. As we know, attention model will teach network to focus on regions of interesting. Figure 4 shows the correct example in test on UCF-11 dataset, where we can see that our model mainly focuses on where the action is happening. Figure 4a is a horse-riding example; our model pays attention to the leg and head of horse, and the leg of human is also given attention. Figure 4b is soccer example; our model mainly attend to the person who is playing soccer as well as the ball. Figure 4c shows two man playing volleyball; our model focuses on the net and surrounding in addition to players. [PERSON] et al. [53] suggest that the true meaning of an action lies in \"'the change or transformation an action brings to the environment\", e.g., kicking a ball. Thus, to recognize action, we not only need the feature from the human, but also need the feature of environment. In other words, we have to combine the foreground and background together to gain better performance. Fortunately, our model tackles this problem by using attention-again model. For example, in Figure 4b, our model not only attends to the person who is playing soccer, but also focuses on the playground. It is naturally believed that, when people judge whether the man in the image is playing soccer, the information of man with a ball is not enough; other contextual information such as playground plays a critical role. Similar to Figure 4b, Figure 4c shows that our model pays more attention to the net and surrounding information. Additionally, it is obvious to observe that the person in Figure 4b,c is not as highlighted as that in Figure 4a.  
Simultaneously, we can also better understand failures of the model using the attention mechanism. Figure 5a shows the incorrect example (Ground truth: biking; Resulttennis), while Figure 5b is correct example of tennis and Figure 5c is correct example of biking. We could understand how the model performs the wrong result to some extent by reviewing Figure 5b.c. As Figure 5b shows, our model attempts to focus on the person playing tennis and some surroundings to recognize \"'tennis\", and the interesting regions are mostly located in the red blocks. In Figure 5c, our model pays attention to the position of bike, apart from region of where the person appears. Meanwhile, the interesting regions actually belong to not the red blocks but the yellow ones. However, we could find that the model only focuses on the person and cannot concentrate on where the bike appears in Figure 5a, thus our model incorrectly classifies the biking into tennis.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **UCF11** & **HMDB51** & **UCF101** \\ \hline Soft attention model [41] & 84.96 & 41.31 & 77 \\ TSN [38] & - & 51. 0 & 84.5 \\ Average pooling [52] & - & 52. 2 & - \\ Attention-again model (our model) & **91.2** & **54.4** & **87.7** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison with state of the art methods, the result from RGB data only. The **bold** text represents the best result.  
Figure 4: \(Cont\).  
Figure 4: The illustration of some correct examples: (**a**) horse-riding example; (**b**) soccer example; and (**c**) volleyball example.  
Figure 5: \(Cont.\)