HCFSLN: Adaptive Hyperbolic Few-Shot Learning for Multimodal Anxiety Detection
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918193646272512 |
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| author | Sneh, Aditya Sahu, Nilesh Kumar Shelke, Anushka Sanjay Adyasha, Arya Lone, Haroon R. |
| author_facet | Sneh, Aditya Sahu, Nilesh Kumar Shelke, Anushka Sanjay Adyasha, Arya Lone, Haroon R. |
| contents | Anxiety disorders impact millions globally, yet traditional diagnosis relies on clinical interviews, while machine learning models struggle with overfitting due to limited data. Large-scale data collection remains costly and time-consuming, restricting accessibility. To address this, we introduce the Hyperbolic Curvature Few-Shot Learning Network (HCFSLN), a novel Few-Shot Learning (FSL) framework for multimodal anxiety detection, integrating speech, physiological signals, and video data. HCFSLN enhances feature separability through hyperbolic embeddings, cross-modal attention, and an adaptive gating network, enabling robust classification with minimal data. We collected a multimodal anxiety dataset from 108 participants and benchmarked HCFSLN against six FSL baselines, achieving 88% accuracy, outperforming the best baseline by 14%. These results highlight the effectiveness of hyperbolic space for modeling anxiety-related speech patterns and demonstrate FSL's potential for anxiety classification. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_06988 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | HCFSLN: Adaptive Hyperbolic Few-Shot Learning for Multimodal Anxiety Detection Sneh, Aditya Sahu, Nilesh Kumar Shelke, Anushka Sanjay Adyasha, Arya Lone, Haroon R. Machine Learning Human-Computer Interaction Anxiety disorders impact millions globally, yet traditional diagnosis relies on clinical interviews, while machine learning models struggle with overfitting due to limited data. Large-scale data collection remains costly and time-consuming, restricting accessibility. To address this, we introduce the Hyperbolic Curvature Few-Shot Learning Network (HCFSLN), a novel Few-Shot Learning (FSL) framework for multimodal anxiety detection, integrating speech, physiological signals, and video data. HCFSLN enhances feature separability through hyperbolic embeddings, cross-modal attention, and an adaptive gating network, enabling robust classification with minimal data. We collected a multimodal anxiety dataset from 108 participants and benchmarked HCFSLN against six FSL baselines, achieving 88% accuracy, outperforming the best baseline by 14%. These results highlight the effectiveness of hyperbolic space for modeling anxiety-related speech patterns and demonstrate FSL's potential for anxiety classification. |
| title | HCFSLN: Adaptive Hyperbolic Few-Shot Learning for Multimodal Anxiety Detection |
| topic | Machine Learning Human-Computer Interaction |
| url | https://arxiv.org/abs/2511.06988 |