Search Results for author: Jiajun Bu

Found 33 papers, 13 papers with code

Collaborate to Adapt: Source-Free Graph Domain Adaptation via Bi-directional Adaptation

1 code implementation3 Mar 2024 Zhen Zhang, Meihan Liu, Anhui Wang, Hongyang Chen, Zhao Li, Jiajun Bu, Bingsheng He

Unsupervised Graph Domain Adaptation (UGDA) has emerged as a practical solution to transfer knowledge from a label-rich source graph to a completely unlabelled target graph.

Contrastive Learning Domain Adaptation

Rethinking Propagation for Unsupervised Graph Domain Adaptation

1 code implementation8 Feb 2024 Meihan Liu, Zeyu Fang, Zhen Zhang, Ming Gu, Sheng Zhou, Xin Wang, Jiajun Bu

Motivated by our empirical analysis, we reevaluate the role of GNNs in graph domain adaptation and uncover the pivotal role of the propagation process in GNNs for adapting to different graph domains.

Domain Adaptation

Less is More: A Closer Look at Semantic-based Few-Shot Learning

no code implementations10 Jan 2024 Chunpeng Zhou, Haishuai Wang, Xilu Yuan, Zhi Yu, Jiajun Bu

To address this, we propose a simple but effective framework for few-shot learning tasks, specifically designed to exploit the textual information and language model.

Few-Shot Learning Language Modelling

Reinforcement Neighborhood Selection for Unsupervised Graph Anomaly Detection

no code implementations9 Dec 2023 Yuanchen Bei, Sheng Zhou, Qiaoyu Tan, Hao Xu, Hao Chen, Zhao Li, Jiajun Bu

To address these issues, we utilize the advantages of reinforcement learning in adaptively learning in complex environments and propose a novel method that incorporates Reinforcement neighborhood selection for unsupervised graph ANomaly Detection (RAND).

Graph Anomaly Detection Representation Learning

Partition Speeds Up Learning Implicit Neural Representations Based on Exponential-Increase Hypothesis

1 code implementation ICCV 2023 Ke Liu, Feng Liu, Haishuai Wang, Ning Ma, Jiajun Bu, Bo Han

Based on this fact, we introduce a simple partition mechanism to boost the performance of two INR methods for image reconstruction: one for learning INRs, and the other for learning-to-learn INRs.

Image Reconstruction Semantic Segmentation

Graph Neural Architecture Search with GPT-4

no code implementations30 Sep 2023 Haishuai Wang, Yang Gao, Xin Zheng, Peng Zhang, Hongyang Chen, Jiajun Bu, Philip S. Yu

In this paper, we integrate GPT-4 into GNAS and propose a new GPT-4 based Graph Neural Architecture Search method (GPT4GNAS for short).

Neural Architecture Search

Homophily-enhanced Structure Learning for Graph Clustering

1 code implementation10 Aug 2023 Ming Gu, Gaoming Yang, Sheng Zhou, Ning Ma, Jiawei Chen, Qiaoyu Tan, Meihan Liu, Jiajun Bu

Graph clustering is a fundamental task in graph analysis, and recent advances in utilizing graph neural networks (GNNs) have shown impressive results.

Clustering Graph Clustering +1

Multi-View Fusion and Distillation for Subgrade Distresses Detection based on 3D-GPR

1 code implementation9 Aug 2023 Chunpeng Zhou, Kangjie Ning, Haishuai Wang, Zhi Yu, Sheng Zhou, Jiajun Bu

To address these challenges, we introduce a novel methodology for the subgrade distress detection task by leveraging the multi-view information from 3D-GPR data.

GPR Knowledge Distillation +1

CPDG: A Contrastive Pre-Training Method for Dynamic Graph Neural Networks

no code implementations6 Jul 2023 Yuanchen Bei, Hao Xu, Sheng Zhou, Huixuan Chi, Haishuai Wang, Mengdi Zhang, Zhao Li, Jiajun Bu

Dynamic graph data mining has gained popularity in recent years due to the rich information contained in dynamic graphs and their widespread use in the real world.

Translate the Beauty in Songs: Jointly Learning to Align Melody and Translate Lyrics

no code implementations28 Mar 2023 Chengxi Li, Kai Fan, Jiajun Bu, Boxing Chen, Zhongqiang Huang, Zhi Yu

Song translation requires both translation of lyrics and alignment of music notes so that the resulting verse can be sung to the accompanying melody, which is a challenging problem that has attracted some interests in different aspects of the translation process.


LORE: Logical Location Regression Network for Table Structure Recognition

1 code implementation7 Mar 2023 Hangdi Xing, Feiyu Gao, Rujiao Long, Jiajun Bu, Qi Zheng, Liangcheng Li, Cong Yao, Zhi Yu

Table structure recognition (TSR) aims at extracting tables in images into machine-understandable formats.

regression Table Recognition

Hilbert Distillation for Cross-Dimensionality Networks

1 code implementation8 Nov 2022 Dian Qin, Haishuai Wang, Zhe Liu, Hongjia Xu, Sheng Zhou, Jiajun Bu

Since the distilled 2D networks are supervised by the curves converted from dimensionally heterogeneous 3D features, the 2D networks are given an informative view in terms of learning structural information embedded in well-trained high-dimensional representations.

A Comprehensive Survey on Deep Clustering: Taxonomy, Challenges, and Future Directions

1 code implementation15 Jun 2022 Sheng Zhou, Hongjia Xu, Zhuonan Zheng, Jiawei Chen, Zhao Li, Jiajun Bu, Jia Wu, Xin Wang, Wenwu Zhu, Martin Ester

Motivated by the tremendous success of deep learning in clustering, one of the most fundamental machine learning tasks, and the large number of recent advances in this direction, in this paper we conduct a comprehensive survey on deep clustering by proposing a new taxonomy of different state-of-the-art approaches.

Clustering Deep Clustering +1

SentiPrompt: Sentiment Knowledge Enhanced Prompt-Tuning for Aspect-Based Sentiment Analysis

no code implementations17 Sep 2021 Chengxi Li, Feiyu Gao, Jiajun Bu, Lu Xu, Xiang Chen, Yu Gu, Zirui Shao, Qi Zheng, Ningyu Zhang, Yongpan Wang, Zhi Yu

We inject sentiment knowledge regarding aspects, opinions, and polarities into prompt and explicitly model term relations via constructing consistency and polarity judgment templates from the ground truth triplets.

Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +3

Efficient Medical Image Segmentation Based on Knowledge Distillation

1 code implementation23 Aug 2021 Dian Qin, Jiajun Bu, Zhe Liu, Xin Shen, Sheng Zhou, Jingjun Gu, Zhijua Wang, Lei Wu, Huifen Dai

To deal with this problem, we propose an efficient architecture by distilling knowledge from well-trained medical image segmentation networks to train another lightweight network.

Image Segmentation Knowledge Distillation +3

Distilling Holistic Knowledge with Graph Neural Networks

1 code implementation ICCV 2021 Sheng Zhou, Yucheng Wang, Defang Chen, Jiawei Chen, Xin Wang, Can Wang, Jiajun Bu

The holistic knowledge is represented as a unified graph-based embedding by aggregating individual knowledge from relational neighborhood samples with graph neural networks, the student network is learned by distilling the holistic knowledge in a contrastive manner.

Knowledge Distillation

Uncertainty-Guided Mixup for Semi-Supervised Domain Adaptation without Source Data

no code implementations14 Jul 2021 Ning Ma, Jiajun Bu, Zhen Zhang, Sheng Zhou

Present domain adaptation methods usually perform explicit representation alignment by simultaneously accessing the source data and target data.

Privacy Preserving Semi-supervised Domain Adaptation +1

Semi-Supervised Hypothesis Transfer for Source-Free Domain Adaptation

no code implementations14 Jul 2021 Ning Ma, Jiajun Bu, Lixian Lu, Jun Wen, Zhen Zhang, Sheng Zhou, Xifeng Yan

Domain Adaptation has been widely used to deal with the distribution shift in vision, language, multimedia etc.

Source-Free Domain Adaptation

Cross-modal Image Retrieval with Deep Mutual Information Maximization

no code implementations10 Mar 2021 Chunbin Gu, Jiajun Bu, Xixi Zhou, Chengwei Yao, Dongfang Ma, Zhi Yu, Xifeng Yan

Prior work usually uses a three-stage strategy to tackle this task: 1) extract the features of the inputs; 2) fuse the feature of the source image and its modified text to obtain fusion feature; 3) learn a similarity metric between the desired image and the source image + modified text by using deep metric learning.

Cross-Modal Retrieval Image Retrieval +3

Cyclic Label Propagation for Graph Semi-supervised Learning

no code implementations24 Nov 2020 Zhao Li, Yixin Liu, Zhen Zhang, Shirui Pan, Jianliang Gao, Jiajun Bu

To overcome these limitations, we introduce a novel framework for graph semi-supervised learning termed as Cyclic Label Propagation (CycProp for abbreviation), which integrates GNNs into the process of label propagation in a cyclic and mutually reinforcing manner to exploit the advantages of both GNNs and LPA.

Node Classification

Matching Text with Deep Mutual Information Estimation

no code implementations9 Mar 2020 Xixi Zhou, Chengxi Li, Jiajun Bu, Chengwei Yao, Keyue Shi, Zhi Yu, Zhou Yu

Our approach, Text matching with Deep Info Max (TIM), is integrated with a procedure of unsupervised learning of representations by maximizing the mutual information between text matching neural network's input and output.

Answer Selection Mutual Information Estimation +3

Hierarchical Graph Pooling with Structure Learning

3 code implementations14 Nov 2019 Zhen Zhang, Jiajun Bu, Martin Ester, Jianfeng Zhang, Chengwei Yao, Zhi Yu, Can Wang

HGP-SL incorporates graph pooling and structure learning into a unified module to generate hierarchical representations of graphs.

Graph Classification Representation Learning

HAHE: Hierarchical Attentive Heterogeneous Information Network Embedding

2 code implementations31 Jan 2019 Sheng Zhou, Jiajun Bu, Xin Wang, Jia-Wei Chen, Can Wang

Second, given a meta path, nodes in HIN are connected by path instances while existing works fail to fully explore the differences between path instances that reflect nodes' preferences in the semantic space.

Network Embedding

Navigation Objects Extraction for Better Content Structure Understanding

no code implementations26 Aug 2017 Kui Zhao, Bangpeng Li, Zilun Peng, Jiajun Bu, Can Wang

Dynamic and personalized elements such as top stories, recommended list in a webpage are vital to the understanding of the dynamic nature of web 2. 0 sites.

Deep Style Match for Complementary Recommendation

no code implementations26 Aug 2017 Kui Zhao, Xia Hu, Jiajun Bu, Can Wang

In order to answer these kinds of questions, we attempt to model human sense of style compatibility in this paper.

Common Sense Reasoning Feature Engineering

Relational Multi-Manifold Co-Clustering

no code implementations16 Nov 2016 Ping Li, Jiajun Bu, Chun Chen, Zhanying He, Deng Cai

In this study, we focus on improving the co-clustering performance via manifold ensemble learning, which is able to maximally approximate the intrinsic manifolds of both the sample and feature spaces.

Clustering Ensemble Learning

Weakly Supervised Multiclass Video Segmentation

no code implementations CVPR 2014 Xiao Liu, DaCheng Tao, Mingli Song, Ying Ruan, Chun Chen, Jiajun Bu

In this paper, we present a novel nearest neighbor-based label transfer scheme for weakly supervised video segmentation.

Segmentation Semantic Similarity +5

Semi-Supervised Coupled Dictionary Learning for Person Re-identification

no code implementations CVPR 2014 Xiao Liu, Mingli Song, DaCheng Tao, Xingchen Zhou, Chun Chen, Jiajun Bu

In this paper, to bridge the human appearance variations across cameras, two coupled dictionaries that relate to the gallery and probe cameras are jointly learned in the training phase from both labeled and unlabeled images.

Dictionary Learning Person Re-Identification

Probabilistic Graphlet Cut: Exploiting Spatial Structure Cue for Weakly Supervised Image Segmentation

no code implementations CVPR 2013 Luming Zhang, Mingli Song, Zicheng Liu, Xiao Liu, Jiajun Bu, Chun Chen

Finally, we propose a novel image segmentation algorithm, called graphlet cut, that leverages the learned graphlet distribution in measuring the homogeneity of a set of spatially structured superpixels.

Image Segmentation Segmentation +2

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