# Multitask Learning-Based Affective Prediction for Videos of Films and TV Scenes
## 5 Conclusions and Future Work  
Considering the fine-grained affective prediction problem of film and TV scene videos, we proposed a multitask prediction model, MHAF-PLE. The algorithm combines dynamic video information with keyframe image information as the input, enhancing the PLE-based gating network with a multi-headed attention mechanism as the primary algorithmic framework. A joint loss function was designed based on factor correlation, ensuring the same learning speed for special affects with strong correlation. The experimental results showed that detecting the presence of characters in the video is an important auxiliary task for affective prediction. MHAF-PLE achieves improved accuracy by adding this task.  
The affective attributes of film and TV video are reflected at a multi-dimensional level, especially the high-dimensional characteristics of cognition and perception. However, they are not clearly determined. In the future, we will continue to explore the hidden visual information representation of time and space of pictures from the perspective of designed artworks. In addition, it would be valuable to find the correlation between objective parameters and perceptual attributes through an artificial model for both prediction and artistic appreciation.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Task** & **Share-Bottom** & **PLE** & **RF. [12]** & **MMTrans-MT [14]** & **MHAF-PLE (Ours)** \\ \hline Warm & 0.2498 & 0.1761 & 0.1619 & 0.2069 & 0.0185 \\ Hopeful & 0.2329 & 0.1870 & 0.1888 & 0.2027 & 0.0465 \\ Happy & 0.2787 & 0.1880 & 0.2252 & 0.2243 & 0.0394 \\ Romantic & 0.2217 & 0.1724 & 0.1868 & 0.1870 & 0.0504 \\ Relaxed & 0.2695 & 0.2315 & 0.1856 & 0.2295 & 0.0465 \\ Fresh & 0.2555 & 0.1733 & 0.2289 & 0.1945 & 0.0501 \\ Cozy & 0.2603 & 0.1810 & 0.1936 & 0.2123 & 0.0444 \\ Sunny & 0.2503 & 0.1891 & 0.1830 & 0.2163 & 0.0523 \\ Magnificent & 0.1341 & 0.1025 & 0.1697 & 0.1601 & 0.0498 \\ Dreamy & 0.1836 & 0.1510 & 0.2062 & 0.1852 & 0.0456 \\ Lonely & 0.2636 & 0.2178 & 0.2126 & 0.1907 & 0.0433 \\ Sentimental & 0.1784 & 0.1578 & 0.2027 & 0.1831 & 0.0328 \\ Disappointed & 0.1780 & 0.1637 & 0.1928 & 0.1698 & 0.0418 \\ Depressed & 0.1900 & 0.1757 & 0.1905 & 0.1933 & 0.0352 \\ Oppressive & 0.2159 & 0.2245 & 0.2147 & 0.1905 & 0.0586 \\ Anxious & 0.2101 & 0.1801 & 0.2038 & 0.2047 & 0.0371 \\ Average & 0.2232 & 0.1794 & 0.1966 & 0.1970 & 0.0432 \\ Character & 80\% & 79\% & 55\% & 59\% & 98\% \\ classification & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 10: Prediction results based on different models.  
**Author Contributions:** Conceptualization, Z.S.; methodology, Z.S., S.L. and W.J.; software, S.L. and Y.F.; validation, S.L. and L.Z.; formal analysis, L.Z. and W.J.; writing--original draft preparation, Z.S. and S.L.; writing--review and editing, Z.S. and L.Z.; All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was funded by the National Natural Science Foundation of China (Grant No. 62276240) and the Fundamental Research Funds for the Central Universities (Grant No. CUC23 ZDTJ010).  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data that support the findings of this study are available from the corresponding author upon reasonable request. The data are not publicly available due to ongoing research.  
**Conflicts of Interest:** The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.