46 citations · 167 across the 72 of their papers we have counts for
75 papers
SkillCome: Group Contrast Skill Optimization with Dual Memory
Haolin Li, Feng Hong, Ang Li +6
Skill evolution improves the capabilities of large language models by analyzing trajectories generated under a given skill and modifying the skill accordingly. Existing approaches…
Dementia Etiology Diagnosis via Collaborative Meta Knowledge Enhancement
Siyuan Du, Mengxi Chen, Xinyang Jiang +6
Although artificial intelligence (AI) has shown promising performance in several medical tasks, accurate dementia etiology diagnosis with AI remains challenging due to complex over…
A Vision-language Framework for Comparative Reasoning in Radiology
Tengfei Zhang, Ziheng Zhao, Xiaoman Zhang +5
Medical imaging artificial intelligence has achieved strong performance in isolated image interpretation, but remains poorly aligned with radiological practice, where diagnosis and…
Evaluating Large Language Models in Dynamic Clinical Decision-Making with Standardized Patient Cases
Cheng Liang, Pengcheng Qiu, Ya Zhang +3
Large language models (LLMs) are increasingly proposed as clinical agents, yet static, single-turn benchmarks cannot capture how a model dynamically delivers care across an encount…
Roll Out and Roll Back: Diffusion LLMs are Their Own Efficiency Teachers
Fanqin Zeng, Feng Hong, Geng Yu +6
Diffusion Large Language Models (DLLMs) promise fast parallel generation, yet open-source DLLMs still face a severe quality-speed trade-off: accelerating decoding by revealing mult…
Deep Reprogramming Distillation for Medical Foundation Models
Siyuan Du, Yuhang Zhou, Haolin Li +5
Medical foundation models pre-trained on large-scale datasets have shown powerful versatile performance. However, when adapting medical foundation models for specific medical scena…
Eliciting Medical Reasoning with Knowledge-enhanced Data Synthesis: A Semi-Supervised Reinforcement Learning Approach
Haolin Li, Shuyang Jiang, Ruipeng Zhang +3
While large language models hold promise for complex medical applications, their development is hindered by the scarcity of high-quality reasoning data. To address this issue, exis…
SpotSound: Enhancing Large Audio-Language Models with Fine-Grained Temporal Grounding
Luoyi Sun, Xiao Zhou, Zeqian Li +3
Large Audio-Language Models (ALMs) have recently demonstrated remarkable capabilities in holistic audio understanding, yet they remain unreliable for temporal grounding, i.e., the…
GenMask: Adapting DiT for Segmentation via Direct Mask Generation
Yuhuan Yang, Xianwei Zhuang, Yuxuan Cai +6
Recent approaches for segmentation have leveraged pretrained generative models as feature extractors, treating segmentation as a downstream adaptation task via indirect feature ret…
Demographic-Aware Self-Supervised Anomaly Detection Pretraining for Equitable Rare Cardiac Diagnosis
Chaoqin Huang, Zi Zeng, Aofan Jiang +6
Rare cardiac anomalies are difficult to detect from electrocardiograms (ECGs) due to their long-tailed distribution with extremely limited case counts and demographic disparities i…
Miner:Mining Intrinsic Mastery for Data-Efficient RL in Large Reasoning Models
Shuyang Jiang, Yuhao Wang, Ya Zhang +2
Current critic-free RL methods for large reasoning models suffer from severe inefficiency when training on positive homogeneous prompts (where all rollouts are correct), resulting…
Decouple before Align: Visual Disentanglement Enhances Prompt Tuning
Fei Zhang, Tianfei Zhou, Jiangchao Yao +3
Prompt tuning (PT), as an emerging resource-efficient fine-tuning paradigm, has showcased remarkable effectiveness in improving the task-specific transferability of vision-language…
Wide-In, Narrow-Out: Revokable Decoding for Efficient and Effective DLLMs
Feng Hong, Geng Yu, Yushi Ye +5
Diffusion Large Language Models (DLLMs) have emerged as a compelling alternative to Autoregressive models, designed for fast parallel generation. However, existing DLLMs are plague…
MoMa: Modulating Mamba for Adapting Image Foundation Models to Video Recognition
Yuhuan Yang, Chaofan Ma, Zhenjie Mao +3
Video understanding is a complex challenge that requires effective modeling of spatial-temporal dynamics. With the success of image foundation models (IFMs) in image understanding,…
ConText: Driving In-context Learning for Text Removal and Segmentation
Fei Zhang, Pei Zhang, Baosong Yang +3
This paper presents the first study on adapting the visual in-context learning (V-ICL) paradigm to optical character recognition tasks, specifically focusing on text removal and se…
G4Seg: Generation for Inexact Segmentation Refinement with Diffusion Models
Tianjiao Zhang, Fei Zhang, Jiangchao Yao +2
This paper considers the problem of utilizing a large-scale text-to-image diffusion model to tackle the challenging Inexact Segmentation (IS) task. Unlike traditional approaches th…
An Agentic System for Rare Disease Diagnosis with Traceable Reasoning
Weike Zhao, Chaoyi Wu, Yanjie Fan +10
Rare diseases affect over 300 million individuals worldwide, yet timely and accurate diagnosis remains an urgent challenge. Patients often endure a prolonged diagnostic odyssey exc…
Rethinking Whole-Body CT Image Interpretation: An Abnormality-Centric Approach
Ziheng Zhao, Lisong Dai, Ya Zhang +2
Automated interpretation of CT images-particularly localizing and describing abnormal findings across multi-plane and whole-body scans-remains a significant challenge in clinical r…
AutoMedEval: Harnessing Language Models for Automatic Medical Capability Evaluation
Xiechi Zhang, Zetian Ouyang, Linlin Wang +6
With the proliferation of large language models (LLMs) in the medical domain, there is increasing demand for improved evaluation techniques to assess their capabilities. However, t…
Learning to Instruct for Visual Instruction Tuning
Zhihan Zhou, Feng Hong, Jiaan Luo +5
We propose L2T, an advancement of visual instruction tuning (VIT). While VIT equips Multimodal LLMs (MLLMs) with promising multimodal capabilities, the current design choices for V…
ChatBEV: Empowering Traffic Scene Understanding and Simulation via Vision-Language Model
Qingyao Xu, Ya Zhang, Yanfeng Wang +1
Comprehensive traffic scene understanding is a foundational capability for Intelligent Transportation Systems (ITS) underpinning applications such as traffic simulation. While Visi…
Contrast-Unity for Partially-Supervised Temporal Sentence Grounding
Haicheng Wang, Chen Ju, Weixiong Lin +4
Temporal sentence grounding aims to detect event timestamps described by the natural language query from given untrimmed videos. The existing fully-supervised setting achieves grea…
Active Sampling for Node Attribute Completion on Graphs
Benyuan Liu, Xu Chen, Yanfeng Wang +3
Node attribute, a type of crucial information for graph analysis, may be partially or completely missing for certain nodes in real world applications. Restoring the missing attribu…
MedS: Towards Medical Slow Thinking with Self-Evolved Soft Dual-sided Process Supervision
Shuyang Jiang, Yusheng Liao, Zhe Chen +3
Medical language models face critical barriers to real-world clinical reasoning applications. However, mainstream efforts, which fall short in task coverage, lack fine-grained supe…