116 citations · 161 across the 17 of their papers we have counts for
18 papers
Exploring Information Seeking Agent Consolidation
Guochen Yan, Jialong Wu, Zhengwei Tao +8
Information-seeking agents have emerged as a powerful paradigm for knowledge-intensive tasks, yet today's systems remain specialized for the open web, documents, or local knowledge…
FedSRD: Sparsify-Reconstruct-Decompose for Communication-Efficient Federated Large Language Models Fine-Tuning
Guochen Yan, Luyuan Xie, Qingni Shen +2
The current paradigm of training large language models (LLMs) on public available Web data is becoming unsustainable as high-quality data sources in specialized domains near exhaus…
Life-Cycle Routing Vulnerabilities of LLM Router
Qiqi Lin, Xiaoyang Ji, Shengfang Zhai +4
Large language models (LLMs) have achieved remarkable success in natural language processing, yet their performance and computational costs vary significantly. LLM routers play a c…
dFLMoE: Decentralized Federated Learning via Mixture of Experts for Medical Data Analysis
Luyuan Xie, Tianyu Luan, Wenyuan Cai +7
Federated learning has wide applications in the medical field. It enables knowledge sharing among different healthcare institutes while protecting patients' privacy. However, exist…
FedVCK: Non-IID Robust and Communication-Efficient Federated Learning via Valuable Condensed Knowledge for Medical Image Analysis
Guochen Yan, Luyuan Xie, Xinyi Gao +4
Federated learning has become a promising solution for collaboration among medical institutions. However, data owned by each institution would be highly heterogeneous and the distr…
Personalized One-shot Federated Graph Learning for Heterogeneous Clients
Guochen Yan, Xunkai Li, Luyuan Xie +3
Federated Graph Learning (FGL) has emerged as a promising paradigm for breaking data silos among distributed private graphs. In practical scenarios involving heterogeneous distribu…
MH-pFLGB: Model Heterogeneous personalized Federated Learning via Global Bypass for Medical Image Analysis
Luyuan Xie, Manqing Lin, ChenMing Xu +7
In the evolving application of medical artificial intelligence, federated learning is notable for its ability to protect training data privacy. Federated learning facilitates colla…
pFLFE: Cross-silo Personalized Federated Learning via Feature Enhancement on Medical Image Segmentation
Luyuan Xie, Manqing Lin, Siyuan Liu +6
In medical image segmentation, personalized cross-silo federated learning (FL) is becoming popular for utilizing varied data across healthcare settings to overcome data scarcity an…
MH-pFLID: Model Heterogeneous personalized Federated Learning via Injection and Distillation for Medical Data Analysis
Luyuan Xie, Manqing Lin, Tianyu Luan +4
Federated learning is widely used in medical applications for training global models without needing local data access. However, varying computational capabilities and network arch…
Text-to-Image Diffusion Models can be Easily Backdoored through Multimodal Data Poisoning
Shengfang Zhai, Yinpeng Dong, Qingni Shen +3
With the help of conditioning mechanisms, the state-of-the-art diffusion models have achieved tremendous success in guided image generation, particularly in text-to-image synthesis…
NCL: Textual Backdoor Defense Using Noise-augmented Contrastive Learning
Shengfang Zhai, Qingni Shen, Xiaoyi Chen +4
At present, backdoor attacks attract attention as they do great harm to deep learning models. The adversary poisons the training data making the model being injected with a backdoo…
Learning 3D Photography Videos via Self-supervised Diffusion on Single Images
Xiaodong Wang, Chenfei Wu, Shengming Yin +9
3D photography renders a static image into a video with appealing 3D visual effects. Existing approaches typically first conduct monocular depth estimation, then render the input f…
A Simple and Effective Method to Improve Zero-Shot Cross-Lingual Transfer Learning
Kunbo Ding, Weijie Liu, Yuejian Fang +6
Existing zero-shot cross-lingual transfer methods rely on parallel corpora or bilingual dictionaries, which are expensive and impractical for low-resource languages. To disengage f…
Multi-stage Distillation Framework for Cross-Lingual Semantic Similarity Matching
Kunbo Ding, Weijie Liu, Yuejian Fang +3
Previous studies have proved that cross-lingual knowledge distillation can significantly improve the performance of pre-trained models for cross-lingual similarity matching tasks.…
NUWA-Infinity: Autoregressive over Autoregressive Generation for Infinite Visual Synthesis
Chenfei Wu, Jian Liang, Xiaowei Hu +6
In this paper, we present NUWA-Infinity, a generative model for infinite visual synthesis, which is defined as the task of generating arbitrarily-sized high-resolution images or lo…
NÜWA: Visual Synthesis Pre-training for Neural visUal World creAtion
Chenfei Wu, Jian Liang, Lei Ji +4
This paper presents a unified multimodal pre-trained model called NÜWA that can generate new or manipulate existing visual data (i.e., images and videos) for various visual synthes…
Hybrid Reasoning Network for Video-based Commonsense Captioning
Weijiang Yu, Jian Liang, Lei Ji +4
The task of video-based commonsense captioning aims to generate event-wise captions and meanwhile provide multiple commonsense descriptions (e.g., attribute, effect and intention)…
Unicoder-VL: A Universal Encoder for Vision and Language by Cross-modal Pre-training
Gen Li, Nan Duan, Yuejian Fang +3
We propose Unicoder-VL, a universal encoder that aims to learn joint representations of vision and language in a pre-training manner. Borrow ideas from cross-lingual pre-trained mo…