Search Results for author: Guangnan Ye

Found 17 papers, 5 papers with code

ArtNVG: Content-Style Separated Artistic Neighboring-View Gaussian Stylization

no code implementations25 Dec 2024 Zixiao Gu, Mengtian Li, Ruhua Chen, Zhongxia Ji, Sichen Guo, Zhenye Zhang, Guangnan Ye, Zuo Hu

Our framework realizes high-quality 3D stylization by incorporating two pivotal techniques: Content-Style Separated Control and Attention-based Neighboring-View Alignment.

3DGS

DiffPatch: Generating Customizable Adversarial Patches using Diffusion Model

no code implementations2 Dec 2024 Zhixiang Wang, Guangnan Ye, Xiaosen Wang, Siheng Chen, Zhibo Wang, Xingjun Ma, Yu-Gang Jiang

However, most existing adversarial patch generation methods prioritize attack effectiveness over stealthiness, resulting in patches that are aesthetically unpleasing.

model

DogLayout: Denoising Diffusion GAN for Discrete and Continuous Layout Generation

1 code implementation30 Nov 2024 Zhaoxing Gan, Guangnan Ye

Layout Generation aims to synthesize plausible arrangements from given elements.

Denoising

Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks

no code implementations20 Nov 2024 Yong Xie, Weijie Zheng, Hanxun Huang, Guangnan Ye, Xingjun Ma

Over the past decade, a large number of white-box adversarial robustness evaluation methods (i. e., attacks) have been proposed, ranging from single-step to multi-step methods and from individual to ensemble methods.

Adversarial Robustness Image Classification

White-box Multimodal Jailbreaks Against Large Vision-Language Models

1 code implementation28 May 2024 Ruofan Wang, Xingjun Ma, Hanxu Zhou, Chuanjun Ji, Guangnan Ye, Yu-Gang Jiang

Subsequently, an adversarial text suffix is integrated and co-optimized with the adversarial image prefix to maximize the probability of eliciting affirmative responses to various harmful instructions.

Adversarial Robustness Adversarial Text

FedCAda: Adaptive Client-Side Optimization for Accelerated and Stable Federated Learning

no code implementations20 May 2024 Liuzhi Zhou, Yu He, Kun Zhai, Xiang Liu, Sen Liu, Xingjun Ma, Guangnan Ye, Yu-Gang Jiang, Hongfeng Chai

This comparative analysis revealed that due to the limited information contained within client models from other clients during the initial stages of federated learning, more substantial constraints need to be imposed on the parameters of the adaptive algorithm.

Federated Learning

The Dog Walking Theory: Rethinking Convergence in Federated Learning

no code implementations18 Apr 2024 Kun Zhai, Yifeng Gao, Xingjun Ma, Difan Zou, Guangnan Ye, Yu-Gang Jiang

In this paper, we study the convergence of FL on non-IID data and propose a novel \emph{Dog Walking Theory} to formulate and identify the missing element in existing research.

Federated Learning

SilverSight: A Multi-Task Chinese Financial Large Language Model Based on Adaptive Semantic Space Learning

no code implementations7 Apr 2024 YuHang Zhou, Zeping Li, Siyu Tian, Yuchen Ni, Sen Liu, Guangnan Ye, Hongfeng Chai

Large language models (LLMs) are increasingly being applied across various specialized fields, leveraging their extensive knowledge to empower a multitude of scenarios within these domains.

Language Modeling Language Modelling +1

RAGFormer: Learning Semantic Attributes and Topological Structure for Fraud Detection

1 code implementation27 Feb 2024 Haolin Li, Shuyang Jiang, Lifeng Zhang, Siyuan Du, Guangnan Ye, Hongfeng Chai

The simple yet effective network consists of a semantic encoder, a topology encoder, and an attention fusion module.

Fraud Detection Relation

$R^3$-NL2GQL: A Model Coordination and Knowledge Graph Alignment Approach for NL2GQL

1 code implementation3 Nov 2023 YuHang Zhou, Yu He, Siyu Tian, Yuchen Ni, Zhangyue Yin, Xiang Liu, Chuanjun Ji, Sen Liu, Xipeng Qiu, Guangnan Ye, Hongfeng Chai

While current tasks of converting natural language to SQL (NL2SQL) using Foundation Models have shown impressive achievements, adapting these approaches for converting natural language to Graph Query Language (NL2GQL) encounters hurdles due to the distinct nature of GQL compared to SQL, alongside the diverse forms of GQL.

Knowledge Graphs Natural Language Queries +3

Multi-Domain Transformer-Based Counterfactual Augmentation for Earnings Call Analysis

no code implementations2 Dec 2021 Zixuan Yuan, Yada Zhu, Wei zhang, Ziming Huang, Guangnan Ye, Hui Xiong

Earnings call (EC), as a periodic teleconference of a publicly-traded company, has been extensively studied as an essential market indicator because of its high analytical value in corporate fundamentals.

counterfactual Data Augmentation

On Sample Based Explanation Methods for NLP: Faithfulness, Efficiency and Semantic Evaluation

no code implementations ACL 2021 Wei zhang, Ziming Huang, Yada Zhu, Guangnan Ye, Xiaodong Cui, Fan Zhang

In the recent advances of natural language processing, the scale of the state-of-the-art models and datasets is usually extensive, which challenges the application of sample-based explanation methods in many aspects, such as explanation interpretability, efficiency, and faithfulness.

On Sample Based Explanation Methods for NLP:Efficiency, Faithfulness, and Semantic Evaluation

1 code implementation9 Jun 2021 Wei zhang, Ziming Huang, Yada Zhu, Guangnan Ye, Xiaodong Cui, Fan Zhang

In the recent advances of natural language processing, the scale of the state-of-the-art models and datasets is usually extensive, which challenges the application of sample-based explanation methods in many aspects, such as explanation interpretability, efficiency, and faithfulness.

Towards Federated Graph Learning for Collaborative Financial Crimes Detection

no code implementations19 Sep 2019 Toyotaro Suzumura, Yi Zhou, Natahalie Baracaldo, Guangnan Ye, Keith Houck, Ryo Kawahara, Ali Anwar, Lucia Larise Stavarache, Yuji Watanabe, Pablo Loyola, Daniel Klyashtorny, Heiko Ludwig, Kumar Bhaskaran

Advances in technology used in this domain, including machine learning based approaches, can improve upon the effectiveness of financial institutions' existing processes, however, a key challenge that most financial institutions continue to face is that they address financial crimes in isolation without any insight from other firms.

Federated Learning Graph Learning

EventNet: A Large Scale Structured Concept Library for Complex Event Detection in Video

no code implementations8 Jun 2015 Guangnan Ye, Yitong Li, Hongliang Xu, Dong Liu, Shih-Fu Chang

Extensive experiments over the zero-shot event retrieval task when no training samples are available show that the EventNet concept library consistently and significantly outperforms the state-of-the-art (such as the 20K ImageNet concepts trained with CNN) by a large margin up to 207%.

Event Detection Retrieval

Sample-Specific Late Fusion for Visual Category Recognition

no code implementations CVPR 2013 Dong Liu, Kuan-Ting Lai, Guangnan Ye, Ming-Syan Chen, Shih-Fu Chang

However, the existing methods generally use a fixed fusion weight for all the scores of a classifier, and thus fail to optimally determine the fusion weight for the individual samples.

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