2 citations · 4 across the 16 of their papers we have counts for
17 papers
DoAtlas-2: A Foundation for Self-Evolving Causal Biomedical Discovery
Yulong Li, Rong Xia, Yuxuan Zhang +12
We introduce DoAtlas-2, a foundation for self-evolving causal biomedical discovery that organizes knowledge around causal mechanisms and advances through external evidence from hum…
Dialectic-Med: Mitigating Diagnostic Hallucinations via Counterfactual Adversarial Multi-Agent Debate
Zhixiang Lu, Jionglong Su
Multimodal Large Language Models (MLLMs) in healthcare suffer from severe confirmation bias, often hallucinating visual details to support initial, potentially erroneous diagnostic…
Attention-Guided Flow-Matching for Sparse 3D Geological Generation
Zhixiang Lu, Mengqi Han, Peixin Guo +4
Constructing high-resolution 3D geological models from sparse 1D borehole and 2D surface data is a highly ill-posed inverse problem. Traditional heuristic and implicit modeling met…
Semantic-Topological Graph Reasoning for Language-Guided Pulmonary Screening
Chenyu Xue, Yiran Liu, Mian Zhou +2
Medical image segmentation driven by free-text clinical instructions is a critical frontier in computer-aided diagnosis. However, existing multimodal and foundation models struggle…
ConFoThinking: Consolidated Focused Attention Driven Thinking for Visual Question Answering
Zhaodong Wu, Haochen Xue, Qi Cao +5
Thinking with Images improves fine-grained VQA for MLLMs by emphasizing visual cues. However, tool-augmented methods depend on the capacity of grounding, which remains unreliable f…
PRISM: A Personality-Driven Multi-Agent Framework for Social Media Simulation
Zhixiang Lu, Xueyuan Deng, Yiran Liu +4
Traditional agent-based models (ABMs) of opinion dynamics often fail to capture the psychological heterogeneity driving online polarization due to simplistic homogeneity assumption…
SAM-DCE: Decoupled Context Enhancement for Prompt-Free Medical Image Segmentation
Yingzhen Hu, Yiheng Zhong, Ruobing Li +8
The Segment Anything Model (SAM) is a promising foundation model for medical image segmentation, but its prompt-driven design is not directly suitable for fully automatic multi-cla…
Seeing Far and Clearly: Mitigating Hallucinations in MLLMs with Attention Causal Decoding
Feilong Tang, Chengzhi Liu, Zhongxing Xu +9
Recent advancements in multimodal large language models (MLLMs) have significantly improved performance in visual question answering. However, they often suffer from hallucinations…
Confidence-Aware Self-Distillation for Multimodal Sentiment Analysis with Incomplete Modalities
Yanxi Luo, Shijin Wang, Zhongxing Xu +3
Multimodal sentiment analysis (MSA) aims to understand human sentiment through multimodal data. In real-world scenarios, practical factors often lead to uncertain modality missingn…
LLM-Enhanced Feature Engineering for Multi-Factor Electricity Price Predictions
Haochen Xue, Chenghao Liu, Chong Zhang +9
Accurately forecasting electricity price volatility is crucial for effective risk management and decision-making. Traditional forecasting models often fall short in capturing the c…
Rhythm of Opinion: A Hawkes-Graph Framework for Dynamic Propagation Analysis
Yulong Li, Zhixiang Lu, Feilong Tang +8
The rapid development of social media has significantly reshaped the dynamics of public opinion, resulting in complex interactions that traditional models fail to effectively captu…
MMRC: A Large-Scale Benchmark for Understanding Multimodal Large Language Model in Real-World Conversation
Haochen Xue, Feilong Tang, Ming Hu +13
Recent multimodal large language models (MLLMs) have demonstrated significant potential in open-ended conversation, generating more accurate and personalized responses. However, th…
KD-MSLRT: Lightweight Sign Language Recognition Model Based on Mediapipe and 3D to 1D Knowledge Distillation
Yulong Li, Bolin Ren, Ke Hu +4
Artificial intelligence has achieved notable results in sign language recognition and translation. However, relatively few efforts have been made to significantly improve the quali…
Decoding the Flow: CauseMotion for Emotional Causality Analysis in Long-form Conversations
Yuxuan Zhang, Yulong Li, Zichen Yu +5
Long-sequence causal reasoning seeks to uncover causal relationships within extended time series data but is hindered by complex dependencies and the challenges of validating causa…
Beyond Words: AuralLLM and SignMST-C for Sign Language Production and Bidirectional Accessibility
Yulong Li, Yuxuan Zhang, Feilong Tang +10
Sign language is the primary communication mode for 72 million hearing-impaired individuals worldwide, necessitating effective bidirectional Sign Language Production and Sign Langu…
Neighbor Does Matter: Density-Aware Contrastive Learning for Medical Semi-supervised Segmentation
Feilong Tang, Zhongxing Xu, Ming Hu +6
In medical image analysis, multi-organ semi-supervised segmentation faces challenges such as insufficient labels and low contrast in soft tissues. To address these issues, existing…
Toward Modality Gap: Vision Prototype Learning for Weakly-supervised Semantic Segmentation with CLIP
Zhongxing Xu, Feilong Tang, Zhe Chen +5
The application of Contrastive Language-Image Pre-training (CLIP) in Weakly Supervised Semantic Segmentation (WSSS) research powerful cross-modal semantic understanding capabilitie…