6 citations · 18 across the 16 of their papers we have counts for
16 papers
Multi-View Synergistic Learning with Vision-Language Adaption for Low-Resource Biomedical Image Classification
Xiaoliu Luo, Minxue Xiao, Ting Xie +5
Accurate biomedical image classification under low-resource conditions remains challenging due to limited annotations, subtle inter-class visual differences, and complex disease se…
AgentSwing: Adaptive Parallel Context Management Routing for Long-Horizon Web Agents
Zhaopeng Feng, Liangcai Su, Zhen Zhang +16
As large language models (LLMs) evolve into autonomous agents for long-horizon information-seeking, managing finite context capacity has become a critical bottleneck. Existing cont…
Hulu-Med: A Transparent Generalist Model towards Holistic Medical Vision-Language Understanding
Songtao Jiang, Yuan Wang, Sibo Song +22
Real-world clinical decision-making requires integrating heterogeneous data, including medical text, 2D images, 3D volumes, and videos, while existing AI systems fail to unify all…
Modest-Align: Data-Efficient Alignment for Vision-Language Models
Jiaxiang Liu, Yuan Wang, Jiawei Du +3
Cross-modal alignment aims to map heterogeneous modalities into a shared latent space, as exemplified by models like CLIP, which benefit from large-scale image-text pretraining for…
DentVLM: A Multimodal Vision-Language Model for Comprehensive Dental Diagnosis and Enhanced Clinical Practice
Zijie Meng, Jin Hao, Xiwei Dai +20
Diagnosing and managing oral diseases necessitate advanced visual interpretation across diverse imaging modalities and integrated information synthesis. While current AI models exc…
Beyond Modality Collapse: Representations Blending for Multimodal Dataset Distillation
Xin Zhang, Ziruo Zhang, Jiawei Du +2
Multimodal Dataset Distillation (MDD) seeks to condense large-scale image-text datasets into compact surrogates while retaining their effectiveness for cross-modal learning. Despit…
DiffPO: Diffusion-styled Preference Optimization for Efficient Inference-Time Alignment of Large Language Models
Ruizhe Chen, Wenhao Chai, Zhifei Yang +5
Inference-time alignment provides an efficient alternative for aligning LLMs with humans. However, these approaches still face challenges, such as limited scalability due to policy…
KPL: Training-Free Medical Knowledge Mining of Vision-Language Models
Jiaxiang Liu, Tianxiang Hu, Jiawei Du +3
Visual Language Models such as CLIP excel in image recognition due to extensive image-text pre-training. However, applying the CLIP inference in zero-shot classification, particula…
MedCoT: Medical Chain of Thought via Hierarchical Expert
Jiaxiang Liu, Yuan Wang, Jiawei Du +2
Artificial intelligence has advanced in Medical Visual Question Answering (Med-VQA), but prevalent research tends to focus on the accuracy of the answers, often overlooking the rea…
Identifying and Mitigating Social Bias Knowledge in Language Models
Ruizhe Chen, Yichen Li, Jianfei Yang +3
Generating fair and accurate predictions plays a pivotal role in deploying large language models (LLMs) in the real world. However, existing debiasing methods inevitably generate u…
A Concise but High-performing Network for Image Guided Depth Completion in Autonomous Driving
Moyun Liu, Bing Chen, Youping Chen +4
Depth completion is a crucial task in autonomous driving, aiming to convert a sparse depth map into a dense depth prediction. Due to its potentially rich semantic information, RGB…
FedLoGe: Joint Local and Generic Federated Learning under Long-tailed Data
Zikai Xiao, Zihan Chen, Liyinglan Liu +6
Federated Long-Tailed Learning (Fed-LT), a paradigm wherein data collected from decentralized local clients manifests a globally prevalent long-tailed distribution, has garnered co…
Fed-GraB: Federated Long-tailed Learning with Self-Adjusting Gradient Balancer
Zikai Xiao, Zihan Chen, Songshang Liu +7
Data privacy and long-tailed distribution are the norms rather than the exception in many real-world tasks. This paper investigates a federated long-tailed learning (Fed-LT) task i…
TSegFormer: 3D Tooth Segmentation in Intraoral Scans with Geometry Guided Transformer
Huimin Xiong, Kunle Li, Kaiyuan Tan +6
Optical Intraoral Scanners (IOS) are widely used in digital dentistry to provide detailed 3D information of dental crowns and the gingiva. Accurate 3D tooth segmentation in IOSs is…
Fast Model Debias with Machine Unlearning
Ruizhe Chen, Jianfei Yang, Huimin Xiong +7
Recent discoveries have revealed that deep neural networks might behave in a biased manner in many real-world scenarios. For instance, deep networks trained on a large-scale face r…
Towards Distribution-Agnostic Generalized Category Discovery
Jianhong Bai, Zuozhu Liu, Hualiang Wang +7
Data imbalance and open-ended distribution are two intrinsic characteristics of the real visual world. Though encouraging progress has been made in tackling each challenge separate…