# Multitask Learning-Based Affective Prediction for Videos of Films and TV Scenes
## 4 Experimental Results
### Results
#### 4.3.3 Algorithm Prediction Results Based on Different Loss Functions  
We designed experiments to verify the effectiveness of the joint loss function combined with FA and the affective weight setting combined with loss means for fine-grained affective prediction. The proposed joint function was compared with different loss weight cases, such as the consistency of all affect loss weights and the comparison of loss weight outlines, with results as shown in Table 8.  
The following three findings can be obtained from the comparison of the experimental results in Table 8. First, in the self-constructed film and TV scene dataset, using the joint loss function that combines FA achieves better performance, reducing the MAE by 0.0026 compared with the setting with consistent weights for all affects. Second, using the weight setting that combines the loss means for each category of affects after a certain number of rounds of training is better than the weight setting that combines only FA but not the loss means after a certain number of rounds of training, reducing the MAE by 0.0017. Third, using a large scaling factor, i.e., a large weight, reduces the MAE by 0.0025 compared with a small weight, and the MAE for each affect is mostly reduced. The best performance,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Number** & **Task** \\ \hline
1 & Positive emotion (warm) predictions + character classification + emotional polarity classification \\
2 & Positive emotion (warm) prediction + negative emotion (disappointed) prediction + character classification \\
3 & Positive emotion (warm and magnificent) prediction + negative emotion prediction (disappointed and depressed) + character classification \\
4 & Positive emotion (warm, magnificent, and sunny) prediction + negative emotion prediction (disappointed and depressed) + character classification \\ \hline \hline \end{tabular}
\end{table}
Table 6: Selected tasks with corresponding numbers.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{5}{c}{**Task Number**} \\ \cline{2-5}
**Predict Result** & **1** & **2** & **3** & **4** & **Proposed Task** \\ \hline Warm & 0.0370 & 0.0270 & 0.0258 & 0.2500 & **0.0248** \\ Sunny & / & / & 0.0781 & 0.0725 & **0.0694** \\ Magnificent & / & / & 0.0600 & 0.0536 & **0.0508** \\ Disappointed & / & 0.0892 & 0.0754 & 0.0720 & **0.0676** \\ Depressed & / & / & / & 0.7567 & **0.6700** \\ Character & 69\% & 71\% & 71\% & 75\% & **97\%** \\ Classification & & & & & \\ Emotional polarity classification & 65\% & / & / & / & / \\ \hline \hline \end{tabular}
\end{table}
Table 7: Prediction results based on different task selection methods.  
highlighted in bold in Table 8, is achieved when using the combination of three types of loss together. Therefore, we choose weights with large weight outlines for further discussion.  
Within a specified range, an increase in the loss weight correlates with decreased MAE for each task. This relationship is evident upon examining the last two columns in Table 8. Specifically, when the weight a is set to 10, the trend of variation in the predictive results for the primary task is delineated in Table 9. The amplification factor k is defined within a range from 1 to 9. As the k progressively increases, the overall MAE reaches its minimum value at k = 5, which is in bold in Table 9. By employing magnification adjustments, it becomes feasible to pinpoint the optimal equilibrium within the primary task of affective prediction and between the primary and auxiliary tasks. However, an excessively large value of k can disrupt this balance, resulting in diminished performance.