Search Results for author: Zhenfeng Zhu

Found 23 papers, 7 papers with code

Unleashing the potential of GNNs via Bi-directional Knowledge Transfer

no code implementations26 Oct 2023 Shuai Zheng, Zhizhe Liu, Zhenfeng Zhu, Xingxing Zhang, JianXin Li, Yao Zhao

On this basis, BiKT not only allows us to acquire knowledge from both the GNN and its derived model but promotes each other by injecting the knowledge into the other.

Domain Adaptation Representation Learning +1

Neural Node Matching for Multi-Target Cross Domain Recommendation

no code implementations12 Feb 2023 Wujiang Xu, Shaoshuai Li, Mingming Ha, Xiaobo Guo, Qiongxu Ma, Xiaolei Liu, Linxun Chen, Zhenfeng Zhu

To tackle the aforementioned issues, we propose a simple-yet-effective neural node matching based framework for more general CDR settings, i. e., only (few) partially overlapped users exist across domains and most overlapped as well as non-overlapped users do have sparse interactions.

Node-oriented Spectral Filtering for Graph Neural Networks

no code implementations7 Dec 2022 Shuai Zheng, Zhenfeng Zhu, Zhizhe Liu, Youru Li, Yao Zhao

Graph neural networks (GNNs) have shown remarkable performance on homophilic graph data while being far less impressive when handling non-homophilic graph data due to the inherent low-pass filtering property of GNNs.

Semi-Supervised Heterogeneous Graph Learning with Multi-level Data Augmentation

no code implementations30 Nov 2022 Ying Chen, Siwei Qiang, Mingming Ha, Xiaolei Liu, Shaoshuai Li, Lingfeng Yuan, Xiaobo Guo, Zhenfeng Zhu

Differing from homogeneous graph, DA in heterogeneous graph has greater challenges: heterogeneity of information requires DA strategies to effectively handle heterogeneous relations, which considers the information contribution of different types of neighbors and edges to the target nodes.

Data Augmentation Graph Learning

HGV4Risk: Hierarchical Global View-guided Sequence Representation Learning for Risk Prediction

1 code implementation15 Nov 2022 Youru Li, Zhenfeng Zhu, Xiaobo Guo, Shaoshuai Li, Yuchen Yang, Yao Zhao

Moreover, the hierarchical representations at both instance level and channel level can be coordinated by the heterogeneous information aggregation under the guidance of global view.

Graph Embedding Representation Learning +1

MHSCNet: A Multimodal Hierarchical Shot-aware Convolutional Network for Video Summarization

1 code implementation18 Apr 2022 Wujiang Xu, Runzhong Wang, Xiaobo Guo, Shaoshuai Li, Qiongxu Ma, Yunan Zhao, Sheng Guo, Zhenfeng Zhu, Junchi Yan

However, the optimal video summaries need to reflect the most valuable keyframe with its own information, and one with semantic power of the whole content.

Video Summarization

Multi-modal Graph Learning for Disease Prediction

1 code implementation11 Mar 2022 Shuai Zheng, Zhenfeng Zhu, Zhizhe Liu, Zhenyu Guo, Yang Liu, Yuchen Yang, Yao Zhao

For disease prediction tasks, most existing graph-based methods tend to define the graph manually based on specified modality (e. g., demographic information), and then integrated other modalities to obtain the patient representation by Graph Representation Learning (GRL).

Disease Prediction Graph Learning +1

CETransformer: Casual Effect Estimation via Transformer Based Representation Learning

no code implementations19 Jul 2021 Zhenyu Guo, Shuai Zheng, Zhizhe Liu, Kun Yan, Zhenfeng Zhu

Treatment effect estimation, which refers to the estimation of causal effects and aims to measure the strength of the causal relationship, is of great importance in many fields but is a challenging problem in practice.

counterfactual Representation Learning +1

Margin Preserving Self-paced Contrastive Learning Towards Domain Adaptation for Medical Image Segmentation

1 code implementation15 Mar 2021 Zhizhe Liu, Zhenfeng Zhu, Shuai Zheng, Yang Liu, Jiayu Zhou, Yao Zhao

To bridge the gap between the source and target domains in unsupervised domain adaptation (UDA), the most common strategy puts focus on matching the marginal distributions in the feature space through adversarial learning.

Cardiac Segmentation Contrastive Learning +4

Adversarial Graph Disentanglement

1 code implementation12 Mar 2021 Shuai Zheng, Zhenfeng Zhu, Zhizhe Liu, Jian Cheng, Yao Zhao

For them, a component-specific aggregation approach is proposed to achieve micro-disentanglement by inferring latent components that cause the links between nodes.

Disentanglement Graph Representation Learning

Taking Modality-free Human Identification as Zero-shot Learning

no code implementations2 Oct 2020 Zhizhe Liu, Xingxing Zhang, Zhenfeng Zhu, Shuai Zheng, Yao Zhao, Jian Cheng

There have been numerous methods proposed for human identification, such as face identification, person re-identification, and gait identification.

Attribute Event Detection +4

From Anchor Generation to Distribution Alignment: Learning a Discriminative Embedding Space for Zero-Shot Recognition

no code implementations10 Feb 2020 Fuzhen Li, Zhenfeng Zhu, Xingxing Zhang, Jian Cheng, Yao Zhao

In zero-shot learning (ZSL), the samples to be classified are usually projected into side information templates such as attributes.

Zero-Shot Learning

To See in the Dark: N2DGAN for Background Modeling in Nighttime Scene

no code implementations12 Dec 2019 Zhenfeng Zhu, Yingying Meng, Deqiang Kong, Xingxing Zhang, Yandong Guo, Yao Zhao

Due to the deteriorated conditions of \mbox{illumination} lack and uneven lighting, nighttime images have lower contrast and higher noise than their daytime counterparts of the same scene, which limits seriously the performances of conventional background modeling methods.

ProLFA: Representative Prototype Selection for Local Feature Aggregation

1 code implementation24 Oct 2019 Xingxing Zhang, Zhenfeng Zhu, Yao Zhao

Given a set of hand-crafted local features, acquiring a global representation via aggregation is a promising technique to boost computational efficiency and improve task performance.

Computational Efficiency Prototype Selection

ATZSL: Defensive Zero-Shot Recognition in the Presence of Adversaries

no code implementations24 Oct 2019 Xingxing Zhang, Shupeng Gui, Zhenfeng Zhu, Yao Zhao, Ji Liu

In this paper, we take an initial attempt, and propose a generic formulation to provide a systematical solution (named ATZSL) for learning a robust ZSL model.

Image Captioning Object Recognition +2

Hierarchical Prototype Learning for Zero-Shot Recognition

no code implementations24 Oct 2019 Xingxing Zhang, Shupeng Gui, Zhenfeng Zhu, Yao Zhao, Ji Liu

Specifically, HPL is able to obtain discriminability on both seen and unseen class domains by learning visual prototypes respectively under the transductive setting.

Attribute Image Captioning +3

Convolutional Prototype Learning for Zero-Shot Recognition

no code implementations22 Oct 2019 Zhizhe Liu, Xingxing Zhang, Zhenfeng Zhu, Shuai Zheng, Yao Zhao, Jian Cheng

The key to ZSL is to transfer knowledge from the seen to the unseen classes via auxiliary class attribute vectors.

Attribute Image Captioning +3

Edge Heuristic GAN for Non-uniform Blind Deblurring

no code implementations11 Jul 2019 Shuai Zheng, Zhenfeng Zhu, Jian Cheng, Yandong Guo, Yao Zhao

Non-uniform blur, mainly caused by camera shake and motions of multiple objects, is one of the most common causes of image quality degradation.

Deblurring Generative Adversarial Network

EA-LSTM: Evolutionary Attention-based LSTM for Time Series Prediction

no code implementations9 Nov 2018 Youru Li, Zhenfeng Zhu, Deqiang Kong, Hua Han, Yao Zhao

To address this issue, an evolutionary attention-based LSTM training with competitive random search is proposed for multivariate time series prediction.

Time Series Time Series Prediction

Modality-dependent Cross-media Retrieval

no code implementations22 Jun 2015 Yunchao Wei, Yao Zhao, Zhenfeng Zhu, Shikui Wei, Yanhui Xiao, Jiashi Feng, Shuicheng Yan

Specifically, by jointly optimizing the correlation between images and text and the linear regression from one modal space (image or text) to the semantic space, two couples of mappings are learned to project images and text from their original feature spaces into two common latent subspaces (one for I2T and the other for T2I).

Retrieval

Kernel Reconstruction ICA for Sparse Representation

no code implementations9 Apr 2013 Yanhui Xiao, Zhenfeng Zhu, Yao Zhao

However, ICA is not only sensitive to whitening but also difficult to learn an over-complete basis.

Image Classification

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