Export Citations
Save this search
Please login to be able to save your searches and receive alerts for new content matching your search criteria.
- research-articleAugust 2024
Multi-modal long document classification based on Hierarchical Prompt and Multi-modal Transformer
AbstractIn the realm of long document classification (LDC), previous research has predominantly focused on modeling unimodal texts, overlooking the potential of multi-modal documents incorporating images. To address this gap, we introduce an innovative ...
- research-articleFebruary 2024
Hierarchical Multi-Modal Prompting Transformer for Multi-Modal Long Document Classification
IEEE Transactions on Circuits and Systems for Video Technology (IEEETCSVT), Volume 34, Issue 7Pages 6376–6390https://doi.org/10.1109/TCSVT.2024.3366935In the context of long document classification (LDC), effectively utilizing multi-modal information encompassing texts and images within these documents has not received adequate attention. This task showcases several notable characteristics. Firstly, the ...
- research-articleMarch 2024
Beyond low-pass filtering on large-scale graphs via Adaptive Filtering Graph Neural Networks
AbstractGraph Neural Networks (GNNs) have emerged as a crucial deep learning framework for graph-structured data. However, existing GNNs suffer from the scalability limitation, which hinders their practical implementation in industrial settings. Many ...
- research-articleFebruary 2024
Hierarchical Multi-Granularity Interaction Graph Convolutional Network for Long Document Classification
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP), Volume 32Pages 1762–1775https://doi.org/10.1109/TASLP.2024.3369530With the growing demand for text analytics, long document classification (LDC) has received extensive attention, and great progress has been made. To reveal the complex structure and extract the intrinsic feature, the current approaches focus on modeling ...
- research-articleNovember 2023
Multi-Level Interaction Based Knowledge Graph Completion
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP), Volume 32Pages 386–396https://doi.org/10.1109/TASLP.2023.3331121With the continuous emergence of new knowledge, Knowledge Graph (KG) typically suffers from the incompleteness problem, hindering the performance of downstream applications. Thus, Knowledge Graph Completion (KGC) has attracted considerable attention. ...
-
- research-articleMay 2023
TDN: Triplet Distributor Network for Knowledge Graph Completion
IEEE Transactions on Knowledge and Data Engineering (IEEECS_TKDE), Volume 35, Issue 12Pages 13002–13014https://doi.org/10.1109/TKDE.2023.3272568Conventional Knowledge Graph Completion (KGC) methods typically map entities and relations to a unified space through the shared mapping matrix, and then interact with entities and relations to infer the missing items in the knowledge graph. Although this ...
- research-articleApril 2023
Robust discriminant analysis with feature selective projection and between-classes structural incoherence
AbstractOur paper proposes a new feature extraction method, named as robust discriminant analysis (RDA), for data classification tasks. Based on linear discriminant analysis (LDA), RDA integrates the feature selection and feature extraction into a ...
- research-articleApril 2023
CaEGCN: Cross-Attention Fusion Based Enhanced Graph Convolutional Network for Clustering
IEEE Transactions on Knowledge and Data Engineering (IEEECS_TKDE), Volume 35, Issue 4Pages 3471–3483https://doi.org/10.1109/TKDE.2021.3125020With the powerful learning ability of deep convolutional networks, deep clustering methods can extract the most discriminative information from individual data and produce more satisfactory clustering results. However, existing deep clustering methods ...
- research-articleApril 2023
Hierarchical Spatio–Temporal Graph Convolutional Networks and Transformer Network for Traffic Flow Forecasting
IEEE Transactions on Intelligent Transportation Systems (ITS-TRANSACTIONS), Volume 24, Issue 4Pages 3855–3867https://doi.org/10.1109/TITS.2023.3234512Graph convolutional networks (GCN) have been applied in the traffic flow forecasting tasks with the graph capability in describing the irregular topology structures of road networks. However, GCN based traffic flow forecasting methods often fail to ...
- research-articleJanuary 2023
Multi-graph Fusion Graph Convolutional Networks with pseudo-label supervision
Neural Networks (NENE), Volume 158, Issue CPages 305–317https://doi.org/10.1016/j.neunet.2022.11.027AbstractGraph convolutional networks (GCNs) have become a popular tool for learning unstructured graph data due to their powerful learning ability. Many researchers have been interested in fusing topological structures and node features to extract the ...
- research-articleDecember 2022
Adaptive graph convolutional clustering network with optimal probabilistic graph
Neural Networks (NENE), Volume 156, Issue CPages 271–284https://doi.org/10.1016/j.neunet.2022.09.017AbstractThe graph convolutional network (GCN)-based clustering approaches have achieved the impressive performance due to strong ability of exploiting the topological structure. The adjacency graph seriously affects the clustering performance, especially ...
- research-articleNovember 2022
Adversarially regularized joint structured clustering network
Information Sciences: an International Journal (ISCI), Volume 615, Issue CPages 136–151https://doi.org/10.1016/j.ins.2022.09.066AbstractDeep clustering has achieved great success as its powerful ability to learn effective representation. Especially, graph network clustering has attracted more and more attention. Considering the great success of Graph Autoencoder (GAE) in encoding ...
- research-articleJuly 2022
Cross-modal alignment with graph reasoning for image-text retrieval
Multimedia Tools and Applications (MTAA), Volume 81, Issue 17Pages 23615–23632https://doi.org/10.1007/s11042-022-12444-8AbstractImage-text retrieval task has received a lot of attention in the modern research field of artificial intelligence. It still remains challenging since image and text are heterogeneous cross-modal data. The key issue of image-text retrieval is how ...
- research-articleApril 2022
Shareability-Exclusivity Representation on Product Grassmann Manifolds for Multi-camera video clustering
Journal of Visual Communication and Image Representation (JVCIR), Volume 84, Issue Chttps://doi.org/10.1016/j.jvcir.2022.103457AbstractWith the rapid popularity of multi-camera networks, one human action is usually captured by multiple cameras located at different angles simultaneously. Multi-camera videos contain the distinct perspectives of one action, therefore multiple views ...
- research-articleFebruary 2022
Dynamic Graph Convolution Network for Traffic Forecasting Based on Latent Network of Laplace Matrix Estimation
IEEE Transactions on Intelligent Transportation Systems (ITS-TRANSACTIONS), Volume 23, Issue 2Pages 1009–1018https://doi.org/10.1109/TITS.2020.3019497Traffic forecasting is a challenging problem in the transportation research field as the complexity and non-stationary changing of the traffic data, thus the key to the issue is how to explore proper spatial and temporal characteristics. Based on this ...
- research-articleNovember 2021
AKM<sup>3</sup>C: Adaptive K-Multiple-Means for Multi-View Clustering
IEEE Transactions on Circuits and Systems for Video Technology (IEEETCSVT), Volume 31, Issue 11Pages 4214–4226https://doi.org/10.1109/TCSVT.2020.3049005With the popularity of cameras and sensors, massive data are captured from various view angles or modalities, which provide abundant complementary information and also bring great challenges for traditional clustering methods. In this article, we propose ...
- research-articleOctober 2021
Complete/incomplete multi‐view subspace clustering via soft block‐diagonal‐induced regulariser
AbstractThis study proposes a novel multi‐view soft block diagonal representation framework for clustering complete and incomplete multi‐view data. First, given that the multi‐view self‐representation model offers better performance in exploring the ...
- ArticleSeptember 2021
Feature Interaction Based Graph Convolutional Networks for Image-Text Retrieval
Artificial Neural Networks and Machine Learning – ICANN 2021Pages 217–229https://doi.org/10.1007/978-3-030-86365-4_18AbstractTo solve the challenge of heterogeneous gap between visual and linguistic data in image-text retrieval task, many methods have been proposed and significant progress has been made. Recently, some works use more refined information of the relation ...
- research-articleJune 2021
Kronecker-decomposable robust probabilistic tensor discriminant analysis
Information Sciences: an International Journal (ISCI), Volume 561, Issue CPages 196–210https://doi.org/10.1016/j.ins.2021.01.054AbstractAs a generative model, probabilistic linear discriminant analysis (PLDA) has achieved good performance in supervised learning tasks. The model incorporates both within-individual and between-individual variation, and remaining unexplained data ...
- ArticleNovember 2020
Reweighted Non-convex Non-smooth Rank Minimization Based Spectral Clustering on Grassmann Manifold
AbstractLow Rank Representation (LRR) based unsupervised clustering methods have achieved great success since these methods could explore low-dimensional subspace structure embedded in original data effectively. The conventional LRR methods generally ...