152 citations · 166 across the 8 of their papers we have counts for
10 papers
DynaBridge: Dynamic Summary-Guided Cross-Task Multimodal Fusion for DASS-Structured Mental Health Assessment
Shiyu Teng, Haichen Yu, Jiaqing Liu +6
Multimodal behavioral analysis offers a scalable approach to assessing depression, anxiety, and stress, yet generic fusion models often ignore the psychometric structure of questio…
Dynamic Summary Generation for Interpretable Multimodal Depression Detection
Shiyu Teng, Jiaqing Liu, Hao Sun +6
Depression remains widely underdiagnosed and undertreated because stigma and subjective symptom ratings hinder reliable screening. To address this challenge, we propose a coarse-to…
Retrieval-Augmented Multimodal Depression Detection
Ruibo Hou, Shiyu Teng, Jiaqing Liu +4
Multimodal deep learning has shown promise in depression detection by integrating text, audio, and video signals. Recent work leverages sentiment analysis to enhance emotional unde…
A Text-Image Fusion Method with Data Augmentation Capabilities for Referring Medical Image Segmentation
Shurong Chai, Rahul Kumar JAIN, Rui Xu +6
Deep learning relies heavily on data augmentation to mitigate limited data, especially in medical imaging. Recent multimodal learning integrates text and images for segmentation, k…
Enhancing Depression Detection with Chain-of-Thought Prompting: From Emotion to Reasoning Using Large Language Models
Shiyu Teng, Jiaqing Liu, Rahul Kumar Jain +5
Depression is one of the leading causes of disability worldwide, posing a severe burden on individuals, healthcare systems, and society at large. Recent advancements in Large Langu…
One Framework to Rule Them All: Unifying Multimodal Tasks with LLM Neural-Tuning
Hao Sun, Yu Song, Jiaqing Liu +3
Large-scale models have exhibited remarkable capabilities across diverse domains, including automated medical services and intelligent customer support. However, as most large mode…
Multimodal Infusion Tuning for Large Models
Hao Sun, Yu Song, Xinyao Yu +3
Recent advancements in large-scale models have showcased remarkable generalization capabilities in various tasks. However, integrating multimodal processing into these models prese…
Modality-invariant and Specific Prompting for Multimodal Human Perception Understanding
Hao Sun, Ziwei Niu, Xinyao Yu +3
Understanding human perceptions presents a formidable multimodal challenge for computers, encompassing aspects such as sentiment tendencies and sense of humor. While various method…
Ladder Fine-tuning approach for SAM integrating complementary network
Shurong Chai, Rahul Kumar Jain, Shiyu Teng +4
Recently, foundation models have been introduced demonstrating various tasks in the field of computer vision. These models such as Segment Anything Model (SAM) are generalized mode…
CubeMLP: An MLP-based Model for Multimodal Sentiment Analysis and Depression Estimation
Hao Sun, Hongyi Wang, Jiaqing Liu +2
Multimodal sentiment analysis and depression estimation are two important research topics that aim to predict human mental states using multimodal data. Previous research has focus…