Search Results for author: Ying Guo

Found 20 papers, 12 papers with code

CustomListener: Text-guided Responsive Interaction for User-friendly Listening Head Generation

no code implementations1 Mar 2024 Xi Liu, Ying Guo, Cheng Zhen, Tong Li, Yingying Ao, Pengfei Yan

To achieve coherence between segments, we design a Past Guided Generation Module (PGG) to maintain the consistency of customized listener attributes through the motion prior, and utilize a diffusion-based structure conditioned on the portrait token and the motion prior to realize the controllable generation.

Cooperation Does Matter: Exploring Multi-Order Bilateral Relations for Audio-Visual Segmentation

1 code implementation11 Dec 2023 Qi Yang, Xing Nie, Tong Li, Pengfei Gao, Ying Guo, Cheng Zhen, Pengfei Yan, Shiming Xiang

For the first time, our framework explores three types of bilateral entanglements within AVS: pixel entanglement, modality entanglement, and temporal entanglement.

Dynamic Brain Transformer with Multi-level Attention for Functional Brain Network Analysis

1 code implementation5 Sep 2023 Xuan Kan, Antonio Aodong Chen Gu, Hejie Cui, Ying Guo, Carl Yang

However, the conventional approach involving static brain network analysis offers limited potential in capturing the dynamism of brain function.

Controllable Guide-Space for Generalizable Face Forgery Detection

no code implementations ICCV 2023 Ying Guo, Cheng Zhen, Pengfei Yan

In this paper, we propose a controllable guide-space (GS) method to enhance the discrimination of different forgery domains, so as to increase the forgery relevance of features and thereby improve the generalization.

Clustering

R-Mixup: Riemannian Mixup for Biological Networks

no code implementations5 Jun 2023 Xuan Kan, Zimu Li, Hejie Cui, Yue Yu, ran Xu, Shaojun Yu, Zilong Zhang, Ying Guo, Carl Yang

Biological networks are commonly used in biomedical and healthcare domains to effectively model the structure of complex biological systems with interactions linking biological entities.

Data Augmentation

Transformer-Based Hierarchical Clustering for Brain Network Analysis

1 code implementation6 May 2023 Wei Dai, Hejie Cui, Xuan Kan, Ying Guo, Sanne van Rooij, Carl Yang

Brain networks, graphical models such as those constructed from MRI, have been widely used in pathological prediction and analysis of brain functions.

Clustering

Simultaneously Optimizing Perturbations and Positions for Black-box Adversarial Patch Attacks

1 code implementation26 Dec 2022 Xingxing Wei, Ying Guo, Jie Yu, Bo Zhang

Extensive experiments are conducted on the Face Recognition (FR) task, and results on four representative FR models show that our method can significantly improve the attack success rate and query efficiency.

Face Recognition Position +2

Learning Task-Aware Effective Brain Connectivity for fMRI Analysis with Graph Neural Networks

1 code implementation1 Nov 2022 Yue Yu, Xuan Kan, Hejie Cui, ran Xu, Yujia Zheng, Xiangchen Song, Yanqiao Zhu, Kun Zhang, Razieh Nabi, Ying Guo, Chao Zhang, Carl Yang

To better adapt GNNs for fMRI analysis, we propose TBDS, an end-to-end framework based on \underline{T}ask-aware \underline{B}rain connectivity \underline{D}AG (short for Directed Acyclic Graph) \underline{S}tructure generation for fMRI analysis.

Time Series Time Series Analysis

Brain Network Transformer

2 code implementations13 Oct 2022 Xuan Kan, Wei Dai, Hejie Cui, Zilong Zhang, Ying Guo, Carl Yang

Human brains are commonly modeled as networks of Regions of Interest (ROIs) and their connections for the understanding of brain functions and mental disorders.

Clustering

Data-Efficient Brain Connectome Analysis via Multi-Task Meta-Learning

1 code implementation9 Jun 2022 Yi Yang, Yanqiao Zhu, Hejie Cui, Xuan Kan, Lifang He, Ying Guo, Carl Yang

Specifically, we propose to meta-train the model on datasets of large sample sizes and transfer the knowledge to small datasets.

Meta-Learning

FBNETGEN: Task-aware GNN-based fMRI Analysis via Functional Brain Network Generation

1 code implementation25 May 2022 Xuan Kan, Hejie Cui, Joshua Lukemire, Ying Guo, Carl Yang

In particular, we formulate (1) prominent region of interest (ROI) features extraction, (2) brain networks generation, and (3) clinical predictions with GNNs, in an end-to-end trainable model under the guidance of particular prediction tasks.

Time Series Time Series Analysis

Identifying Critical LMS Features for Predicting At-risk Students

no code implementations27 Apr 2022 Ying Guo, Cengiz Gunay, Sairam Tangirala, David Kerven, Wei Jin, Jamye Curry Savage, Seungjin Lee

Unsupervised learning was also used to group students into different clusters based on the similarities in their interaction/involvement with LMS.

Management

Generating Transferable Adversarial Patch by Simultaneously Optimizing its Position and Perturbations

no code implementations29 Sep 2021 Xingxing Wei, Ying Guo, Jie Yu, Huanqian Yan, Bo Zhang

In this paper, we propose a method to simultaneously optimize the position and perturbation to generate transferable adversarial patches, and thus obtain high attack success rates in the black-box setting.

Face Recognition Position

Effective and Interpretable fMRI Analysis via Functional Brain Network Generation

no code implementations23 Jul 2021 Xuan Kan, Hejie Cui, Ying Guo, Carl Yang

Recent studies in neuroscience show great potential of functional brain networks constructed from fMRI data for popularity modeling and clinical predictions.

Improving Adversarial Transferability with Gradient Refining

1 code implementation11 May 2021 Guoqiu Wang, Huanqian Yan, Ying Guo, Xingxing Wei

To improve the transferability of adversarial examples for the black-box setting, several methods have been proposed, e. g., input diversity, translation-invariant attack, and momentum-based attack.

Adversarial Attack Translation

Adversarial Sticker: A Stealthy Attack Method in the Physical World

1 code implementation14 Apr 2021 Xingxing Wei, Ying Guo, Jie Yu

Unlike the previous adversarial patches by designing perturbations, our method manipulates the sticker's pasting position and rotation angle on the objects to perform physical attacks.

Face Recognition Image Retrieval +4

LOCUS: A Novel Decomposition Method for Brain Network Connectivity Matrices using Low-rank Structure with Uniform Sparsity

no code implementations19 Aug 2020 Yikai Wang, Ying Guo

In this paper, we propose a novel blind source separation method with low-rank structure and uniform sparsity (LOCUS) as a fully data-driven decomposition method for network measures.

blind source separation

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