# Multitask Learning-Based Affective Prediction for Videos of Films and TV Scenes
## 4 Experimental Results
### Results
#### 4.3.2 Algorithm Prediction Results Based on Different Task Selection Methods  
To verify the effectiveness of the proposed multitask learning task settings and the applicability of multiple tasks on the proposed model, different emotion tasks were selected for comparison, along with the introduction of other auxiliary tasks. We selected the positive emotion \"warmth\" as the central emotion, which performed well and added main tasks for predicting positive or negative emotions based on it. Considering the balance between the number of positive and negative emotion tasks, an equal proportion of emotion polarity tasks were added. For the selection of auxiliary tasks, we considered commonly used emotion polarity classification tasks. Because characters and scenery are the main subjects in film and television scene videos, we considered the classification of whether characters are present. The selected tasks are shown in Table 6. Video feature extraction uniformly employs the pre-trained TimeSformer model, and features are uniformly selected as fused features of image and video. Within the framework of multitask learning, we  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & \multicolumn{3}{c}{**TimeSformer + CLIP**} \\ \cline{3-5}  & \multicolumn{2}{c}{**First Frame**} & \multicolumn{2}{c}{**Frame**} \\ \cline{2-5}
**Component** & **TimeSformer** & **TimeSformer + Clip** & **First + Last** & **First + Middle + Last** \\ \hline Warm & 0.0522 & 0.0248 & 0.0270 & 0.0254 \\ Hopeful & 0.1754 & 0.0713 & 0.0606 & 0.0621 \\ Happy & 0.1936 & 0.0715 & 0.0548 & 0.0565 \\ Romantic & 0.1613 & 0.0545 & 0.0507 & 0.0529 \\ Relaxed & 0.2080 & 0.0793 & 0.0571 & 0.0597 \\ Fresh & 0.1646 & 0.0577 & 0.0581 & 0.0593 \\ Cozy & 0.1877 & 0.0581 & 0.0514 & 0.0527 \\ Sunny & 0.1901 & 0.0694 & 0.0589 & 0.0602 \\ Magnificent & 0.1331 & 0.0508 & 0.0472 & 0.0481 \\ Dreamy & 0.1613 & 0.0526 & 0.0472 & 0.0513 \\ Lonely & 0.1887 & 0.0707 & 0.0643 & 0.0661 \\ Sentimental & 0.1556 & 0.0661 & 0.0643 & 0.0650 \\ Disappointed & 0.1536 & 0.0676 & 0.0546 & 0.0551 \\ Depressed & 0.1518 & 0.0670 & 0.0519 & 0.0523 \\ Oppressive & 0.1686 & 0.0737 & 0.0506 & 0.0643 \\ Anxious & 0.1731 & 0.0651 & 0.0580 & 0.0612 \\ Average (MAE) & 0.1637 & 0.0694 & **0.0542** & 0.0558 \\ Classification (\%) & 76\% & 97\% & **98\%** & 97\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Prediction results based on different feature selection methods.  
adopt an expert-gating sharing mechanism to share the underlying feature representation. The experiments are conducted on a self-constructed dataset of film and television scene videos, the corresponding prediction results are shown in Table 7.  
As can be seen in Table 7, with the increase in the number of tasks, the MAE for each emotion task decreases, and the prediction effect is significantly improved; similarly, the accuracy of the binary categorization of character landscapes increases with the increase in the number of emotion tasks. This shows that the proposed model is suitable for fine-grained emotion prediction tasks with a large number of tasks and multitask learning of multiple emotion tasks, sharing more information between emotion tasks and improving the overall effect of the model. As indicated in bold in Table 7, when the number of tasks is 17, the MAE for each emotion task decreases, and the accuracy of the binary categorization of character landscapes reaches 97%. Therefore, the proposed model was based on 17 tasks for carrying out research.