15 citations · 28 across the 27 of their papers we have counts for
27 papers
CoANeRV: Coordinate-Aware Token-Space Neural Video Representation
Jialong Guo, Ke Liu, Mengxuan Li +2
Neural representations for videos (NeRV) have shown strong reconstruction fidelity by storing video-specific information in network weights. However, existing formulations typicall…
CAER: Conflict-Aware Evidence Routing with Dual Prefix Experts for Multimodal Large Language Models
Zixuan Liu, Juntao Cai, Xiaoxu Cai +2
Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities in multimodal understanding and generation. However, when textual inputs conflict with visual evi…
DeepBD: A Grounded Agentic Workflow for Variant Prioritization and Diagnosis of Genetic Birth Defects
Shiyu Li, Ziqi Yan, Zhihao Wu +7
Birth defects are a major cause of fetal loss, neonatal morbidity and long-term disability. In the subset with suspected genetic etiologies, exome and genome sequencing have moved…
Morphology-Aware Multimodal Representation Learning for Insect Phylogenetic Reconstruction
Zixuan Liu, Kaijie Yu, Chun He +5
Morphological traits provide important evidence for phylogenetic reconstruction and evolutionary relationship analysis. Recent image-based approaches have introduced deep learning,…
Sparsity Curse: Understanding RLVR Model Parameter Space from Model Merging
Chenrui Wu, Zexi Li, Jiajun Bu +2
Reinforcement Learning with Verifiable Reward (RLVR) has emerged as a powerful post-training paradigm that surpasses Supervised Fine-Tuning (SFT) in eliciting reasoning intelligenc…
Medical world models: representing medical states, modelling clinical dynamics and guiding intervention policies
Ke Liu, Mengxuan Li, Yanyi Bao +4
Medical diagnosis and treatment are dynamic processes in which patient states evolve over time and clinical interventions alter future outcomes. Although current medical AI can det…
Beyond the Individual: Virtualizing Multi-Disciplinary Reasoning for Clinical Intake via Collaborative Agents
Huangwei Chen, Wu Li, Junhao Jia +7
The initial outpatient consultation is critical for clinical decision-making, yet it is often conducted by a single physician under time pressure, making it prone to cognitive bias…
Sci-Mind: Cognitively-Inspired Adversarial Debate for Autonomous Mathematical Modeling
Junhao Jia, Huangwei Chen, Ruiying Sun +4
Real-world mathematical modeling is inherently an experiential and collaborative endeavor. Domain experts rarely solve complex problems from scratch; instead, they draw upon analog…
Learning Image-Text Matching with Optimal Partial Transport
Zhengxin Pan, Haishuai Wang, Fangyu Wu +3
Cross-modal matching, a fundamental task in bridging vision and language, has recently garnered substantial research interest. Despite the development of numerous methods aimed at…
Bi-TEAM: A Unified Cross-Scale Representation Learning Framework for Chemically Modified Biomolecules
Chunbin Gu, Zijun Gao, Mutian He +8
Representation learning for protein biochemical space faces a difficult trade-off: protein language models excel at capturing long-range biological semantics but often miss fine-gr…
LLM-Bootstrapped Targeted Finding Guidance for Factual MLLM-based Medical Report Generation
Cunyuan Yang, Dejuan Song, Xiaotao Pang +7
The automatic generation of medical reports utilizing Multimodal Large Language Models (MLLMs) frequently encounters challenges related to factual instability, which may manifest a…
Unsupervised Causal Prototypical Networks for De-biased Interpretable Dermoscopy Diagnosis
Junhao Jia, Yueyi Wu, Huangwei Chen +4
Despite the success of deep learning in dermoscopy image analysis, its inherent black-box nature hinders clinical trust, motivating the use of prototypical networks for case-based…
Directed Ordinal Diffusion Regularization for Progression-Aware Diabetic Retinopathy Grading
Huangwei Chen, Junhao Jia, Ruocheng Li +7
Diabetic Retinopathy (DR) progresses as a continuous and irreversible deterioration of the retina, following a well-defined clinical trajectory from mild to severe stages. However,…
VasGuideNet: Vascular Topology-Guided Couinaud Liver Segmentation with Structural Contrastive Loss
Chaojie Shen, Jingjun Gu, Zihao Zhao +4
Accurate Couinaud liver segmentation is critical for preoperative surgical planning and tumor localization.However, existing methods primarily rely on image intensity and spatial l…
Multi-Omics Analysis for Cancer Subtype Inference via Unrolling Graph Smoothness Priors
Jielong Lu, Zhihao Wu, Jiajun Yu +2
Integrating multi-omics datasets through data-driven analysis offers a comprehensive understanding of the complex biological processes underlying various diseases, particularly can…
Hubness Reduction with Dual Bank Sinkhorn Normalization for Cross-Modal Retrieval
Zhengxin Pan, Haishuai Wang, Fangyu Wu +2
The past decade has witnessed rapid advancements in cross-modal retrieval, with significant progress made in accurately measuring the similarity between cross-modal pairs. However,…
ImputeINR: Time Series Imputation via Implicit Neural Representations for Disease Diagnosis with Missing Data
Mengxuan Li, Ke Liu, Jialong Guo +3
Healthcare data frequently contain a substantial proportion of missing values, necessitating effective time series imputation to support downstream disease diagnosis tasks. However…
One Head Eight Arms: Block Matrix based Low Rank Adaptation for CLIP-based Few-Shot Learning
Chunpeng Zhou, Qianqian Shen, Zhi Yu +2
Recent advancements in fine-tuning Vision-Language Foundation Models (VLMs) have garnered significant attention for their effectiveness in downstream few-shot learning tasks.While…
MetaNeRV: Meta Neural Representations for Videos with Spatial-Temporal Guidance
Jialong Guo, Ke liu, Jiangchao Yao +3
Neural Representations for Videos (NeRV) has emerged as a promising implicit neural representation (INR) approach for video analysis, which represents videos as neural networks wit…
TSINR: Capturing Temporal Continuity via Implicit Neural Representations for Time Series Anomaly Detection
Mengxuan Li, Ke Liu, Hongyang Chen +3
Time series anomaly detection aims to identify unusual patterns in data or deviations from systems' expected behavior. The reconstruction-based methods are the mainstream in this t…
Attention Beats Linear for Fast Implicit Neural Representation Generation
Shuyi Zhang, Ke Liu, Jingjun Gu +4
Implicit Neural Representation (INR) has gained increasing popularity as a data representation method, serving as a prerequisite for innovative generation models. Unlike gradient-b…
NoisyGL: A Comprehensive Benchmark for Graph Neural Networks under Label Noise
Zhonghao Wang, Danyu Sun, Sheng Zhou +4
Graph Neural Networks (GNNs) exhibit strong potential in node classification task through a message-passing mechanism. However, their performance often hinges on high-quality node…
Towards a Unified Framework of Clustering-based Anomaly Detection
Zeyu Fang, Ming Gu, Sheng Zhou +4
Unsupervised Anomaly Detection (UAD) plays a crucial role in identifying abnormal patterns within data without labeled examples, holding significant practical implications across v…
Guarding Graph Neural Networks for Unsupervised Graph Anomaly Detection
Yuanchen Bei, Sheng Zhou, Jinke Shi +3
Unsupervised graph anomaly detection aims at identifying rare patterns that deviate from the majority in a graph without the aid of labels, which is important for a variety of real…