6 papers
Multimodal Prompt Learning with Irregular EHRs for Robust Monitoring of Critical Care Patients
Yixin Yang, Yueyang Sun, Weichen Liu +2
Accurate assessment of patients in intensive care units (ICUs) is essential for timely clinical intervention and improved patient outcomes. Multimodal electronic health records (EH…
Dep-LLM: Training-Free Depression Diagnosis via Evidence-Guided Structured Multi-factor with Reliable LLM Reasoning
Yiqing Lyu, Xianbing Zhao, Buzhou Tang +1
Automatic Depression Detection (ADD) from clinical interviews is a pivotal task in computational mental health, yet it remains challenging due to two critical obstacles: 1) difficu…
Towards Multimodal Sentiment Analysis via Contrastive Cross-modal Retrieval Augmentation and Hierachical Prompts
Xianbing Zhao, Shengzun Yang, Buzhou Tang +1
Multimodal sentiment analysis is a fundamental problem in the field of affective computing. Although significant progress has been made in cross-modal interaction, it remains a cha…
Physics-Grounded Motion Forecasting via Equation Discovery for Trajectory-Guided Image-to-Video Generation
Tao Feng, Xianbing Zhao, Zhenhua Chen +4
Recent advances in diffusion-based and autoregressive video generation models have achieved remarkable visual realism. However, these models typically lack accurate physical alignm…
Predicting Depression in Screening Interviews from Interactive Multi-Theme Collaboration
Xianbing Zhao, Yiqing Lyu, Di Wang +1
Automatic depression detection provides cues for early clinical intervention by clinicians. Clinical interviews for depression detection involve dialogues centered around multiple…
Learning in Order! A Sequential Strategy to Learn Invariant Features for Multimodal Sentiment Analysis
Xianbing Zhao, Lizhen Qu, Tao Feng +2
This work proposes a novel and simple sequential learning strategy to train models on videos and texts for multimodal sentiment analysis. To estimate sentiment polarities on unseen…