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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2508.11362 |
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| _version_ | 1866911106183725056 |
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| author | Wang, Honghong Wang, Yankai Zhang, Dejun Deng, Jing Zheng, Rong |
| author_facet | Wang, Honghong Wang, Yankai Zhang, Dejun Deng, Jing Zheng, Rong |
| contents | This paper presents Fosafer approach to the Track 2 Mandarin in the Multimodal Emotion and Intent Joint Understandingchallenge, which focuses on achieving joint recognition of emotion and intent in Mandarin, despite the issue of category imbalance. To alleviate this issue, we use a variety of data augmentation techniques across text, video, and audio modalities. Additionally, we introduce the SampleWeighted Focal Contrastive loss, designed to address the challenges of recognizing minority class samples and those that are semantically similar but difficult to distinguish. Moreover, we fine-tune the Hubert model to adapt the emotion and intent joint recognition. To mitigate modal competition, we introduce a modal dropout strategy. For the final predictions, a plurality voting approach is used to determine the results. The experimental results demonstrate the effectiveness of our method, which achieves the second-best performance in the Track 2 Mandarin challenge. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_11362 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Mitigating Category Imbalance: Fosafer System for the Multimodal Emotion and Intent Joint Understanding Challenge Wang, Honghong Wang, Yankai Zhang, Dejun Deng, Jing Zheng, Rong Sound Audio and Speech Processing This paper presents Fosafer approach to the Track 2 Mandarin in the Multimodal Emotion and Intent Joint Understandingchallenge, which focuses on achieving joint recognition of emotion and intent in Mandarin, despite the issue of category imbalance. To alleviate this issue, we use a variety of data augmentation techniques across text, video, and audio modalities. Additionally, we introduce the SampleWeighted Focal Contrastive loss, designed to address the challenges of recognizing minority class samples and those that are semantically similar but difficult to distinguish. Moreover, we fine-tune the Hubert model to adapt the emotion and intent joint recognition. To mitigate modal competition, we introduce a modal dropout strategy. For the final predictions, a plurality voting approach is used to determine the results. The experimental results demonstrate the effectiveness of our method, which achieves the second-best performance in the Track 2 Mandarin challenge. |
| title | Mitigating Category Imbalance: Fosafer System for the Multimodal Emotion and Intent Joint Understanding Challenge |
| topic | Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2508.11362 |