# Fitting Different Interactive Information: Joint Classification of Emotion and Intention

arXiv: https://arxiv.org/abs/2501.06215 · HTML: https://mlsanity.com/paper/2501.06215/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 10 references that are in the map.
- First posted on arXiv 5 Jan 2025.

## Builds on (most cited first, 6 of 6)

- [MERBench: A Unified Evaluation Benchmark for Multimodal Emotion Recognition](https://mlsanity.com/paper/2401.03429/) (2024)
- [Learning to Rebalance Multi-Modal Optimization by Adaptively Masking Subnetworks](https://mlsanity.com/paper/2404.08347/) (2024)
- [Emotion and Intent Joint Understanding in Multimodal Conversation: A Benchmarking Dataset](https://mlsanity.com/paper/2407.02751/) (2024)
- [TalkNet: Fully-Convolutional Non-Autoregressive Speech Synthesis Model](https://mlsanity.com/paper/2005.05514/) (2020)
- [Leveraging Contrastive Learning and Self-Training for Multimodal Emotion Recognition with Limited Labeled Samples](https://mlsanity.com/paper/2409.04447/) (2024)
- [Multimodal Classification via Modal-Aware Interactive Enhancement](https://mlsanity.com/paper/2407.04587/) (2024)

## Abstract (arXiv)

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.