5 citations · 5 across the 6 of their papers we have counts for
7 papers
Disentangled Double Machine Learning for Accurate Causal Effect Estimation
Guodu Xiang, Kui Yu, Yujie Wang +3
Confounding bias is a key challenge in causal effect estimation from observational data. Double Machine Learning (DML) addresses this issue by estimating treatment and outcome nuis…
Differential Mental Disorder Detection with Psychology-Inspired Multimodal Stimuli
Zhiyuan Zhou, Jingjing Wu, Zhibo Lei +9
Differential diagnosis of mental disorders remains a fundamental challenge in real-world clinical practice, where multiple conditions often exhibit overlapping symptoms. However, m…
Biomedical Relation Extraction via Adaptive Document-Relation Cross-Mapping and Concept Unique Identifier
Yufei Shang, Yanrong Guo, Shijie Hao +1
Document-Level Biomedical Relation Extraction (Bio-RE) aims to identify relations between biomedical entities within extensive texts, serving as a crucial subfield of biomedical te…
InterMind: Doctor-Patient-Family Interactive Depression Assessment Empowered by Large Language Models
Zhiyuan Zhou, Jilong Liu, Sanwang Wang +3
Depression poses significant challenges to patients and healthcare organizations, necessitating efficient assessment methods. Existing paradigms typically focus on a patient-doctor…
SDI-Net: Toward Sufficient Dual-View Interaction for Low-light Stereo Image Enhancement
Linlin Hu, Ao Sun, Shijie Hao +2
Currently, most low-light image enhancement methods only consider information from a single view, neglecting the correlation between cross-view information. Therefore, the enhancem…
Towards Multimodal Emotional Support Conversation Systems
Yuqi Chu, Lizi Liao, Zhiyuan Zhou +2
The integration of conversational artificial intelligence (AI) into mental health care promises a new horizon for therapist-client interactions, aiming to closely emulate the depth…
Controllable Relation Disentanglement for Few-Shot Class-Incremental Learning
Yuan Zhou, Richang Hong, Yanrong Guo +3
In this paper, we propose to tackle Few-Shot Class-Incremental Learning (FSCIL) from a new perspective, i.e., relation disentanglement, which means enhancing FSCIL via disentanglin…