5 papers
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…
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…
Toward Robust Multimodal Learning using Multimodal Foundational Models
Xianbing Zhao, Soujanya Poria, Xuejiao Li +2
Existing multimodal sentiment analysis tasks are highly rely on the assumption that the training and test sets are complete multimodal data, while this assumption can be difficult…