用词典法补全缺失图像，零参数实现高精度抑郁检测
VIS-DICT: A Visual Dictionary for Missing Modality Imputation in Social Network Depression Detection
- 通过词-平均图像向量映射构建视觉词典，无需训练生成图像
- 在512条用户时间线数据上达到F1=0.9454，ROC-AUC=0.9890
- 适合资源受限场景，可快速部署于真实社交网络抑郁监测
追踪社交媒体发帖有助于发现抑郁症早期迹象。研究表明，结合文本与图像的多模态方法比仅用文本更有效。然而，许多帖子缺乏图像，使多模态模型难以应用。现有方法通常依赖需额外训练的检索或生成模型来填补缺失图像。本文提出Vis-Dict，一种基于词典的方法：将词汇与完整训练数据中图像的平均向量关联，生成缺失视觉特征，并与文本融合以追踪用户行为变化。我们在包含最多512条帖子的社交媒体数据集上测试了Vis-Dict，结果表明其性能媲美生成网络，取得F1-score 0.9454，ROC-AUC 0.9890。更重要的是，该方法在图像生成部分无任何可训练参数。研究证实，直接将词汇映射至视觉特征是处理抑郁检测中缺失图像的有效且实用方案。
原文摘要 · Abstract (English)
Tracking social media posts can help spot early signs of depression. Recent studies show that combining text and images works better for detecting depression than using text alone. However, many social media posts do not have images, which makes it hard to use multimodal models. Most existing methods fill in missing images using retrieval or generative models that need extra training. In this paper, we introduce Vis-Dict, a dictionary-based method that builds missing visual features by linking words to average image vectors from complete training posts. These estimated visual features are then combined with text to track changes in user behavior over time. We tested Vis-Dict on a social media dataset using user timelines of up to 512 posts and compared it with other missing-data methods. The results show that Vis-Dict performs on par with generative networks, reaching an F1-score of 0.9454 and an ROC-AUC of 0.9890. Most importantly, Vis-Dict achieves this strong performance with zero trainable parameters for image generation. These findings show that directly connecting words to visual features is an effective and practical way to handle missing images in depression detection systems.
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