探索用于多模态抑郁症检测的机器学习与语言模型
Exploring Machine Learning and Language Models for Multimodal Depression Detection
AI总结：
面向首届多模态人格感知抑郁症检测挑战赛，论文比较 XGBoost、Transformer 架构与 LLM 在音视频和文本特征上的检测表现，总结各类模型优劣及多模态表征启示。
摘要：
本文介绍了我们在首届多模态人格感知抑郁症检测挑战赛中的方法，重点关注利用机器学习和深度学习模型进行多模态抑郁症检测。我们在音频、视频和文本特征上探索并比较了 XGBoost、基于 transformer 的架构以及大型语言模型（LLM）的性能。结果揭示了各类模型在跨模态捕捉抑郁相关信号方面的优势与局限，为心理健康预测中有效的多模态表征策略提供了洞见。
英文摘要：
This paper presents our approach to the first Multimodal Personality-Aware Depression Detection Challenge, focusing on multimodal depression detection using machine learning and deep learning models. We explore and compare the performance of XGBoost, transformer-based architectures, and large language models (LLMs) on audio, video, and text features. Our results highlight the strengths and limitations of each type of model in capturing depression-related signals across modalities, offering insights into effective multimodal representation strategies for mental health prediction.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
