This paper is the first-place solution for ICASSP MEIJU@2025 Track I, which focuses on low-resource multimodal emotion and intention recognition. How to effectively utilize a large amount of unlabeled data, while ensuring the mutual promotion of different difficulty levels tasks in the interaction stage, these two points become the key to the competition. In this paper, pseudo-label labeling is carried out on the model trained with labeled data, and samples with high confidence and their labels are selected to alleviate the problem of low resources. At the same time, the characteristic of easy represented ability of intention recognition found in the experiment is used to make mutually promote with emotion recognition under different attention heads, and higher performance of intention recognition is achieved through fusion. Finally, under the refined processing data, we achieve the score of 0.5532 in the Test set, and win the championship of the track.

翻译：本文是ICASSP MEIJU@2025 Track I的冠军解决方案，该赛道专注于低资源多模态情感与意图识别。如何有效利用大量未标注数据，同时确保交互阶段不同难度任务之间的相互促进，这两点成为本次竞赛的关键。本文首先利用已标注数据训练的模型进行伪标签标注，并筛选高置信度样本及其标签以缓解资源不足问题。同时，利用实验中发现的意图识别易于表征的特性，使其在不同注意力头下与情感识别任务相互促进，并通过融合策略实现更优的意图识别性能。最终，在精细化处理的数据上，我们在测试集上取得了0.5532的得分，赢得了该赛道的冠军。