## 5 Conclusions  
This study proposed an approach to the multimodal fusion problem from two perspectives: employing mid-fusion in the deep learning model and using the adaptive loss function. This work proposed XMBT to accommodate datasets with more than two modalities and the DRA objective function for multimodal learning. The proposed framework significantly enhances the emotion recognition task across the IEMOCAP, CMU-MOSEI, and CH-SIMS benchmarks. The proposed framework can effectively be extended to alternative applications, such as intelligent learning environments, medical diagnosis, and mental health assessment, where multimodal understanding is essential. For instance, intelligent tutoring systems can dynamically adjust instructional content based on a learner's emotional state, while in healthcare, it can facilitate the early detection of affective disorders through automated multimodal analysis. In future work, we aim to explore the effectiveness of modality-specific label generation modules and their weighted-adjustment strategies when combined with XMBT. The proposed framework has potential in other multimodal learning tasks, such as highlight detection or video moment retrieval. These avenues warrant further investigation to harness the capabilities of the proposed approach in diverse multimodal applications.  
All authors contributed to the study conception and design. [PERSON]-[PERSON], [PERSON], [PERSON]-H.K. and S.K. performed the material preparation, data analysis, and model experiments. [PERSON]-[PERSON] wrote the first draft of the manuscript. [PERSON]-[PERSON] supervised and administered the project. All authors have read and agreed to the published version of the manuscript.  
This work was partly supported by the Institute of Information & Communications Technology Planning & Evaluation(IITP)-Innovative Human Resource Development for Local Intellectualization program grant funded by the Korea government(MSIT)(IITP-2025-RS-2022-00156287, 33%). This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (RS-2023-00219107). This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) under the Artificial Intelligence Convergence Innovation Human Resources Development (IITP-2023-RS-2023-00256629) grant funded by the Korea government (MSIT).  
Not applicable.  
Figure 8: T-SNE visualization of modal-specific representations of IEMOCAP samples using (**upper**) conventional loss function and (**lower**) DRA loss function.  
Data Availability Statement: Publicly available datasets were analyzed in this study. These data are located as follows: IEMOCAP ([[https://sail.usc.edu/iemocap/](https://sail.usc.edu/iemocap/)]([https://sail.usc.edu/iemocap/](https://sail.usc.edu/iemocap/)), accessed on 6 February 2023), CMU-MOSEI ([[http://multicomp.cs.cmu.edu/resources/cmu-mosei-dataset/](http://multicomp.cs.cmu.edu/resources/cmu-mosei-dataset/)]([http://multicomp.cs.cmu.edu/resources/cmu-mosei-dataset/](http://multicomp.cs.cmu.edu/resources/cmu-mosei-dataset/)), accessed on 6 February 2023), and CH-SIMS ([[https://github.com/thuiar/MMSA](https://github.com/thuiar/MMSA)]([https://github.com/thuiar/MMSA](https://github.com/thuiar/MMSA)), accessed on 6 February 2023).  
The authors declare they have no financial interests.