2 citations · 4 across the 12 of their papers we have counts for
13 papers
DISCERN: Identifiable Disease Evidence for Generalizable Medical Multimodal Learning
Xiaoguang Zhu, Pengyi Jiang, Juncen Guo +3
Clinical multimodal models can learn enrollment shortcuts shared across modalities and fail on new cohorts. We model these data through a latent structural causal model: disease st…
Semantic Cooperative Games for Contribution Attribution in LLM-Based Multi-Agent Systems
Pengyi Jiang, Xiaoguang Zhu, Quanyan Zhu
Contribution attribution has become a central problem in LLM-based multi-agent systems, where final outputs are produced through multiple agents, message exchanges, and ordered wor…
Robust Embodied Perception in Dynamic Environments via Disentangled Weight Fusion
Juncen Guo, Xiaoguang Zhu, Jingyi Wu +4
Embodied perception systems face severe challenges of dynamic environment distribution drift when they continuously interact in open physical spaces. However, the existing domain i…
Diffusion-Guided Semantic Consistency for Multimodal Heterogeneity
Jing Liu, Zhengliang Guo, Yan Wang +4
Federated learning (FL) is severely challenged by non-independent and identically distributed (non-IID) client data, a problem that degrades global model performance, especially in…
Embracing Aleatoric Uncertainty in Medical Multimodal Learning with Missing Modalities
Linxiao Gong, Yang Liu, Lianlong Sun +3
Medical multimodal learning faces significant challenges with missing modalities prevalent in clinical practice. Existing approaches assume equal contribution of modality and rando…
Robust Multimodal Representation Learning in Healthcare
Xiaoguang Zhu, Linxiao Gong, Lianlong Sun +3
Medical multimodal representation learning aims to integrate heterogeneous data into unified patient representations to support clinical outcome prediction. However, real-world med…
M2S2L: Mamba-based Multi-Scale Spatial-temporal Learning for Video Anomaly Detection
Yang Liu, Boan Chen, Xiaoguang Zhu +3
Video anomaly detection (VAD) is an essential task in the image processing community with prospects in video surveillance, which faces fundamental challenges in balancing detection…
Causal Debiasing Medical Multimodal Representation Learning with Missing Modalities
Xiaoguang Zhu, Lianlong Sun, Yang Liu +4
Medical multimodal representation learning aims to integrate heterogeneous clinical data into unified patient representations to support predictive modeling, which remains an essen…
Adaptive Weighted Parameter Fusion with CLIP for Class-Incremental Learning
Juncen Guo, Xiaoguang Zhu, Liangyu Teng +4
Class-incremental Learning (CIL) enables the model to incrementally absorb knowledge from new classes and build a generic classifier across all previously encountered classes. When…
CalFuse: Multi-Modal Continual Learning via Feature Calibration and Parameter Fusion
Juncen Guo, Siao Liu, Xiaoguang Zhu +8
With the proliferation of multi-modal data in large-scale visual recognition systems, enabling models to continuously acquire knowledge from evolving data streams while preserving…
Privacy-Preserving Video Anomaly Detection: A Survey
Yang Liu, Siao Liu, Xiaoguang Zhu +7
Video Anomaly Detection (VAD) aims to automatically analyze spatiotemporal patterns in surveillance videos collected from open spaces to detect anomalous events that may cause harm…
Learning Appearance-motion Normality for Video Anomaly Detection
Yang Liu, Jing Liu, Mengyang Zhao +3
Video anomaly detection is a challenging task in the computer vision community. Most single task-based methods do not consider the independence of unique spatial and temporal patte…
Supplementing Missing Visions via Dialog for Scene Graph Generations
Zhenghao Zhao, Ye Zhu, Xiaoguang Zhu +2
Most current AI systems rely on the premise that the input visual data are sufficient to achieve competitive performance in various computer vision tasks. However, the classic task…