Search Results for author: Jihong Guan

Found 19 papers, 5 papers with code

All in One: Multi-Task Prompting for Graph Neural Networks (Extended Abstract)

no code implementations11 Mar 2024 Xiangguo Sun, Hong Cheng, Jia Li, Bo Liu, Jihong Guan

This paper is an extended abstract of our original work published in KDD23, where we won the best research paper award (Xiangguo Sun, Hong Cheng, Jia Li, Bo Liu, and Jihong Guan.


Molecular Property Prediction Based on Graph Structure Learning

no code implementations28 Dec 2023 Bangyi Zhao, Weixia Xu, Jihong Guan, Shuigeng Zhou

Following that, we conduct graph structure learning on the MSG (i. e., molecule-level graph structure learning) to get the final molecular embeddings, which are the results of fusing both GNN encoded molecular representations and the relationships among molecules, i. e., combining both intra-molecule and inter-molecule information.

Drug Discovery Graph structure learning +2

scRNA-seq Data Clustering by Cluster-aware Iterative Contrastive Learning

1 code implementation27 Dec 2023 Weikang Jiang, Jinxian Wang, Jihong Guan, Shuigeng Zhou

CICL consists of a Transformer encoder, a clustering head, a projection head and a contrastive loss module.

Clustering Contrastive Learning +1

IDDR-NGP: Incorporating Detectors for Distractors Removal with Instant Neural Radiance Field

1 code implementation ACM Multimedia 2023 Xianliang Huang, Jiajie Gou, Shuhang Chen, Zhizhou Zhong, Jihong Guan, Shuigeng Zhou

To validate the effectiveness and robustness of IDDR-NGP, we provide a wide range of distractors with corresponding annotated labels added to both realistic and synthetic scenes.

3D Inpainting Multi-View 3D Reconstruction +1

Diffusing on Two Levels and Optimizing for Multiple Properties: A Novel Approach to Generating Molecules with Desirable Properties

no code implementations5 Oct 2023 Siyuan Guo, Jihong Guan, Shuigeng Zhou

Extensive experiments with two benchmark datasets QM9 and ZINC250k show that the molecules generated by our proposed method have better validity, uniqueness, novelty, Fr\'echet ChemNet Distance (FCD), QED, and PlogP than those generated by current SOTA models.

HiREN: Towards Higher Supervision Quality for Better Scene Text Image Super-Resolution

no code implementations31 Jul 2023 Minyi Zhao, Yi Xu, Bingjia Li, Jie Wang, Jihong Guan, Shuigeng Zhou

Observing the quality issue of HR images, in this paper we propose a novel idea to boost STISR by first enhancing the quality of HR images and then using the enhanced HR images as supervision to do STISR.

Image Generation Image Super-Resolution

Flexible Differentially Private Vertical Federated Learning with Adaptive Feature Embeddings

1 code implementation26 Jul 2023 Yuxi Mi, Hongquan Liu, Yewei Xia, Yiheng Sun, Jihong Guan, Shuigeng Zhou

The emergence of vertical federated learning (VFL) has stimulated concerns about the imperfection in privacy protection, as shared feature embeddings may reveal sensitive information under privacy attacks.

Vertical Federated Learning

Spatial-Temporal Data Mining for Ocean Science: Data, Methodologies, and Opportunities

no code implementations20 Jul 2023 Hanchen Yang, Wengen Li, Shuyu Wang, Hui Li, Jihong Guan, Shuigeng Zhou, Jiannong Cao

Compared with typical ST data (e. g., traffic data), ST ocean data is more complicated but with unique characteristics, e. g., diverse regionality and high sparsity.

Anomaly Detection Event Detection

All in One: Multi-task Prompting for Graph Neural Networks

1 code implementation4 Jul 2023 Xiangguo Sun, Hong Cheng, Jia Li, Bo Liu, Jihong Guan

Inspired by the prompt learning in natural language processing (NLP), which has presented significant effectiveness in leveraging prior knowledge for various NLP tasks, we study the prompting topic for graphs with the motivation of filling the gap between pre-trained models and various graph tasks.


Molecular Property Prediction by Semantic-invariant Contrastive Learning

no code implementations13 Mar 2023 Ziqiao Zhang, Ailin Xie, Jihong Guan, Shuigeng Zhou

Contrastive learning have been widely used as pretext tasks for self-supervised pre-trained molecular representation learning models in AI-aided drug design and discovery.

Contrastive Learning Molecular Property Prediction +3

Hierarchical Few-Shot Object Detection: Problem, Benchmark and Method

1 code implementation8 Oct 2022 Lu Zhang, Yang Wang, Jiaogen Zhou, Chenbo Zhang, Yinglu Zhang, Jihong Guan, Yatao Bian, Shuigeng Zhou

In this paper, we propose and solve a new problem called hierarchical few-shot object detection (Hi-FSOD), which aims to detect objects with hierarchical categories in the FSOD paradigm.

Contrastive Learning Few-Shot Object Detection +2

EPiDA: An Easy Plug-in Data Augmentation Framework for High Performance Text Classification

no code implementations NAACL 2022 Minyi Zhao, Lu Zhang, Yi Xu, Jiandong Ding, Jihong Guan, Shuigeng Zhou

However, to the best of our knowledge, most existing methods consider only either the diversity or the quality of augmented data, thus cannot fully mine the potential of DA for NLP.

Data Augmentation text-classification +1

Recent Few-Shot Object Detection Algorithms: A Survey with Performance Comparison

no code implementations27 Mar 2022 Tianying Liu, Lu Zhang, Yang Wang, Jihong Guan, Yanwei Fu, Jiajia Zhao, Shuigeng Zhou

To this end, the Few-Shot Object Detection (FSOD) has been topical recently, as it mimics the humans' ability of learning to learn, and intelligently transfers the learned generic object knowledge from the common heavy-tailed, to the novel long-tailed object classes.

Few-Shot Object Detection Meta-Learning +3

Identifying Backdoor Attacks in Federated Learning via Anomaly Detection

no code implementations9 Feb 2022 Yuxi Mi, Yiheng Sun, Jihong Guan, Shuigeng Zhou

For instance, studies have revealed that federated learning is vulnerable to backdoor attacks, whereby a compromised participant can stealthily modify the model's behavior in the presence of backdoor triggers.

Federated Learning Privacy Preserving +1

Accurate Few-Shot Object Detection With Support-Query Mutual Guidance and Hybrid Loss

no code implementations CVPR 2021 Lu Zhang, Shuigeng Zhou, Jihong Guan, Ji Zhang

Most object detection methods require huge amounts of annotated data and can detect only the categories that appear in the training set.

Few-Shot Object Detection object-detection

Learning Competitive and Discriminative Reconstructions for Anomaly Detection

no code implementations17 Mar 2019 Kai Tian, Shuigeng Zhou, Jianping Fan, Jihong Guan

Most of the existing methods for anomaly detection use only positive data to learn the data distribution, thus they usually need a pre-defined threshold at the detection stage to determine whether a test instance is an outlier.

Anomaly Detection

Global Semantic Consistency for Zero-Shot Learning

no code implementations22 Jun 2018 Fan Wu, Kai Tian, Jihong Guan, Shuigeng Zhou

In this paper, we propose an end-to-end framework, called Global Semantic Consistency Network (GSC-Net for short), which makes complete use of the semantic information of both seen and unseen classes, to support effective zero-shot learning.

Attribute Generalized Zero-Shot Learning +1

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