1 code implementation • 17 Jun 2024 • Muhao Xu, Zhenfeng Zhu, Youru Li, Shuai Zheng, Yawei Zhao, Kunlun He, Yao Zhao
Multimodal electronic health record (EHR) data can offer a holistic assessment of a patient's health status, supporting various predictive healthcare tasks.
no code implementations • 11 Mar 2024 • Dong Chen, Shuai Zheng, Muhao Xu, Zhenfeng Zhu, Yao Zhao
In the domain of dynamic graph representation learning (DGRL), the efficient and comprehensive capture of temporal evolution within real-world networks is crucial.
no code implementations • CVPR 2024 • Wenjun Hui, Zhenfeng Zhu, Shuai Zheng, Yao Zhao
The Segment Anything Model (SAM) a prompt-driven foundational model has demonstrated remarkable performance in natural image segmentation.
no code implementations • 26 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.
no code implementations • 12 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.
1 code implementation • 7 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.
no code implementations • 30 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.
1 code implementation • 15 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.
1 code implementation • 18 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.
1 code implementation • 11 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).
no code implementations • 19 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.
no code implementations • 1 Jul 2021 • Shuai Zheng, Zhenfeng Zhu, Zhizhe Liu, Zhenyu Guo, Yang Liu, Yao Zhao
However, it is not easy for these approaches to generalize to unseen samples.
1 code implementation • 15 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.
1 code implementation • 12 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.
no code implementations • 2 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.
no code implementations • 10 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.
no code implementations • 12 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.
1 code implementation • CVPR 2020 • Shuai Zheng, Zhenfeng Zhu, Xingxing Zhang, Zhizhe Liu, Jian Cheng, Yao Zhao
Graph representation learning aims to encode all nodes of a graph into low-dimensional vectors that will serve as input of many compute vision tasks.
Generative Adversarial Network
Graph Representation Learning
no code implementations • 24 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.
no code implementations • 24 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.
1 code implementation • 24 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.
no code implementations • 22 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.
no code implementations • 11 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.
no code implementations • 9 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.
no code implementations • 22 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).
no code implementations • 9 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.