8 citations · 17 across the 22 of their papers we have counts for
22 papers
PREBA: Surgical Duration Prediction via PCA-Weighted Retrieval-Augmented LLMs and Bayesian Averaging Aggregation
Wanyin Wu, Kanxue Li, Baosheng Yu +4
Accurate prediction of surgical duration is pivotal for hospital resource management. Although recent supervised learning approaches-from machine learning (ML) to fine-tuned large…
Towards Reliable Medical LLMs: Benchmarking and Enhancing Confidence Estimation of Large Language Models in Medical Consultation
Zhiyao Ren, Yibing Zhan, Siyuan Liang +3
Large-scale language models (LLMs) often offer clinical judgments based on incomplete information, increasing the risk of misdiagnosis. Existing studies have primarily evaluated co…
Cross-Sample Augmented Test-Time Adaptation for Personalized Intraoperative Hypotension Prediction
Kanxue Li, Yibing Zhan, Hua Jin +3
Intraoperative hypotension (IOH) poses significant surgical risks, but accurate prediction remains challenging due to patient-specific variability. While test-time adaptation (TTA)…
Bi-Level Optimization for Self-Supervised AI-Generated Face Detection
Mian Zou, Nan Zhong, Baosheng Yu +2
AI-generated face detectors trained via supervised learning typically rely on synthesized images from specific generators, limiting their generalization to emerging generative tech…
ProGMLP: A Progressive Framework for GNN-to-MLP Knowledge Distillation with Efficient Trade-offs
Weigang Lu, Ziyu Guan, Wei Zhao +5
GNN-to-MLP (G2M) methods have emerged as a promising approach to accelerate Graph Neural Networks (GNNs) by distilling their knowledge into simpler Multi-Layer Perceptrons (MLPs).…
SCAWaveNet: A Spatial-Channel Attention-Based Network for Global Significant Wave Height Retrieval
Chong Zhang, Xichao Liu, Yibing Zhan +3
Recent advancements in spaceborne GNSS missions have produced extensive global datasets, providing a robust basis for deep learning-based significant wave height (SWH) retrieval. W…
Re-Initialization Token Learning for Tool-Augmented Large Language Models
Chenghao Li, Liu Liu, Baosheng Yu +2
Large language models have demonstrated exceptional performance, yet struggle with complex tasks such as numerical reasoning, plan generation. Integrating external tools, such as c…
Reverse Prompt: Cracking the Recipe Inside Text-to-Image Generation
Zhiyao Ren, Yibing Zhan, Baosheng Yu +1
Text-to-image generation has become increasingly popular, but achieving the desired images often requires extensive prompt engineering. In this paper, we explore how to decode text…
Self-Supervised Learning for Detecting AI-Generated Faces as Anomalies
Mian Zou, Baosheng Yu, Yibing Zhan +1
The detection of AI-generated faces is commonly approached as a binary classification task. Nevertheless, the resulting detectors frequently struggle to adapt to novel AI face gene…
Leveraging Metamemory Agent for Enhanced Data-Free Code Generation in Large Language Models
Shengsheng Zhou, Shuai Wang, Liang Ding +5
Large language models (LLMs) have shown strong performance in automated code generation, with few-shot prompting widely used for its simplicity and effectiveness. However, few-shot…
AGMixup: Adaptive Graph Mixup for Semi-supervised Node Classification
Weigang Lu, Ziyu Guan, Wei Zhao +4
Mixup is a data augmentation technique that enhances model generalization by interpolating between data points using a mixing ratio in the image domain. Recently, the concept o…
Joint Input and Output Coordination for Class-Incremental Learning
Shuai Wang, Yibing Zhan, Yong Luo +4
Incremental learning is nontrivial due to severe catastrophic forgetting. Although storing a small amount of data on old tasks during incremental learning is a feasible solution, c…
Semantics-Oriented Multitask Learning for DeepFake Detection: A Joint Embedding Approach
Mian Zou, Baosheng Yu, Yibing Zhan +2
In recent years, the multimedia forensics and security community has seen remarkable progress in multitask learning for DeepFake (i.e., face forgery) detection. The prevailing appr…
Harnessing the Power of MLLMs for Transferable Text-to-Image Person ReID
Wentao Tan, Changxing Ding, Jiayu Jiang +3
Text-to-image person re-identification (ReID) retrieves pedestrian images according to textual descriptions. Manually annotating textual descriptions is time-consuming, restricting…
Semantic Contextualization of Face Forgery: A New Definition, Dataset, and Detection Method
Mian Zou, Baosheng Yu, Yibing Zhan +2
In recent years, deep learning has greatly streamlined the process of manipulating photographic face images. Aware of the potential dangers, researchers have developed various tool…
Towards Training A Chinese Large Language Model for Anesthesiology
Zhonghai Wang, Jie Jiang, Yibing Zhan +8
Medical large language models (LLMs) have gained popularity recently due to their significant practical utility. However, most existing research focuses on general medicine, and th…
Affective Computing for Healthcare: Recent Trends, Applications, Challenges, and Beyond
Yuanyuan Liu, Ke Wang, Lin Wei +4
Affective computing, which aims to recognize, interpret, and understand human emotions, provides benefits in healthcare, such as improving patient care and enhancing doctor-patient…
Healthcare Copilot: Eliciting the Power of General LLMs for Medical Consultation
Zhiyao Ren, Yibing Zhan, Baosheng Yu +2
The copilot framework, which aims to enhance and tailor large language models (LLMs) for specific complex tasks without requiring fine-tuning, is gaining increasing attention from…
Towards Alleviating Text-to-Image Retrieval Hallucination for CLIP in Zero-shot Learning
Hanyao Wang, Yibing Zhan, Liu Liu +3
Pretrained cross-modal models, for instance, the most representative CLIP, have recently led to a boom in using pre-trained models for cross-modal zero-shot tasks, considering the…
Large Language Models as an Indirect Reasoner: Contrapositive and Contradiction for Automated Reasoning
Yanfang Zhang, Yiliu Sun, Yibing Zhan +3
Recently, increasing attention has been focused on improving the ability of Large Language Models (LLMs) to perform complex reasoning. Advanced methods, such as Chain-of-Thought (C…
SpliceMix: A Cross-scale and Semantic Blending Augmentation Strategy for Multi-label Image Classification
Lei Wang, Yibing Zhan, Leilei Ma +3
Recently, Mix-style data augmentation methods (e.g., Mixup and CutMix) have shown promising performance in various visual tasks. However, these methods are primarily designed for s…
Chasing Consistency in Text-to-3D Generation from a Single Image
Yichen Ouyang, Wenhao Chai, Jiayi Ye +3
Text-to-3D generation from a single-view image is a popular but challenging task in 3D vision. Although numerous methods have been proposed, existing works still suffer from the in…