## 4 Results
### Ablation Experiments  
In this section, we evaluate the functionality of the different parts of our action recognition network. We use the same 5-fold cross-validation as in the section before.  
In Table 3, we show the impact of the masked input. We use the masked input frames as input for SlowFast and MViT and also use regular (non-masked) input frames for MAROON. The mask information improves the results for all models on all datasets by 10 percentage points on average. However, our action recognition network MAROON achieves the best (or at least an equally good) result compared to the other approaches, both with and without masks. Particularly, for the Rolandsed Daylight dataset, where the mask information has the greatest influence, the result of MAROON is more than 11 percentage points better than those of SlowFast and MViT. On all datasets, the MViT transformer model benefits the least relatively from the addition of the mask information. This can be explained by the way transformers work. Transformers use an attention mechanism and therefore look at small image sections. The masking results in larger, uniformly black-colored areas that look the same or contain only little image information of the animal when entered into the transformer.  
Furthermore, we analyze the importance of the lateral connection between the MAROON slow pathway and the MAROON fast pathway. In Table 4, we show the results for different possible connections. The authors of [22] also determine the importance of the lateral connection between their SlowFast slow pathway and SlowFast fast pathway. In our evalution, we consider four different possibilities: the connection between the fast and slow pathways (that is the final choice for MAROON), the connection between the medium and slow pathways (before and after merging with the fast pathway), and no connection at all. For the Rolandsede Daylight and Bavarian Forest Nighttime datasets, the fast to slow connection performs best. For the Bavarian Forest Daylight dataset, the fast to slow connection is the second-best choice after the medium (before merging) to slow connection. Overall, the introduction of the third pathway in particular seems to be helpful, and the type of connection is not so decisive. However, since the no connection option is never the best choice, combining the features from the other streams with the new MAROON slow pathway seems to be a promising option. This also corresponds to our theoretical considerations in Section 3.2.1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Model**} & **Top-1** & **Top-5** & \multirow{2}{*}{**Top-1 (with** **Mask**)**} & **Top-5 (with** **Mask**)** \\  & & **(without** & **(without** & **(with** **Mask**) & **(with** **Mask**) \\  & & **Mask**) & & & \\ \hline \multirow{2}{*}{\begin{tabular}{c} Rolandsede \\ Daylight \\ \end{tabular} } & MAROON & 54.65 & 90.96 & **69.19** & **96.31** \\  & SlowFast & 42.05 & 89.66 & 57.67 & 92.48 \\  & MViT & 43.13 & 85.18 & 49.41 & 89.57 \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} Bavarian \\ Forest \\ Daylight \\ \end{tabular} } & MAROON & 36.44 & 94.85 & **46.39** & **97.24** \\  & SlowFast & 35.40 & 95.88 & 45.35 & 96.57 \\  & MViT & 35.05 & 94.17 & 42.63 & 95.54 \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Bavarian \\ Forest \\ Nighttime \\ \end{tabular} } & MAROON & 35.04 & 91.42 & **43.05** & **96.33** \\  & SlowFast & 35.49 & 95.68 & 42.93 & 93.86 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Analysis of the impact of masked input frames. The best value for each dataset is marked in bold.