{"ID":2843898,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.06988","arxiv_id":"2511.06988","title":"HCFSLN: Adaptive Hyperbolic Few-Shot Learning for Multimodal Anxiety Detection","abstract":"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.","short_abstract":"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 Curvatu...","url_abs":"https://arxiv.org/abs/2511.06988","url_pdf":"https://arxiv.org/pdf/2511.06988v1","authors":"[\"Aditya Sneh\",\"Nilesh Kumar Sahu\",\"Anushka Sanjay Shelke\",\"Arya Adyasha\",\"Haroon R. Lone\"]","published":"2025-11-10T11:38:41Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.HC\"]","methods":"[]","has_code":false}