no code implementations • 16 Apr 2024 • Zhengyang Liang, Meiyu Liang, Wei Huang, Yawen Li, Zhe Xue
Our methodology streamlines pre-trained multimodal large models using only their output features and original image-level information, requiring minimal computational resources.
no code implementations • 3 Nov 2023 • Yangxi Zhou, Junping Du, Zhe Xue, Zhenhui Pan, Weikang Chen
This model can combine the epidemic situation data of various provinces for cooperative training to use as an enhanced learning model for epidemic situation decision, while protecting the privacy of data.
no code implementations • 2 Nov 2023 • Junfu Wang, Yawen Li, Zhe Xue, Ang Li
Academic networks in the real world can usually be described by heterogeneous information networks composed of multi-type nodes and relationships.
no code implementations • 1 Nov 2023 • Runze Fang, Yawen Li, Yingxia Shao, Zeli Guan, Zhe Xue
The entity alignment of science and technology patents aims to link the equivalent entities in the knowledge graph of different science and technology patent data sources.
no code implementations • 1 Nov 2023 • Hongrui Gao, Yawen Li, Meiyu Liang, Zeli Guan, Zhe Xue
At the same time, in order to enrich the features of scientific literature, a learning method of semantic representation of scientific literature based on adaptive features and graph neural network is proposed.
no code implementations • 22 Jun 2023 • Jie Gao, Yawen Li, Zhe Xue, Zeli Guan
It can also ensure the load balancing of distributed storage while maintaining spatio-temporal proximity of the data partitioning results.
2 code implementations • ACM Multimedia 2022 • Meiyu Liang, Junping Du, Xiaowen Cao, Yang Yu, Kangkang Lu, Zhe Xue, Min Zhang
Secondly, for further improving learning ability of implicit cross-media semantic associations, a semantic label association graph is constructed, and the graph convolutional network is utilized to mine the implicit semantic structures, thus guiding learning of discriminative features of different modalities.
1 code implementation • ACM International Conference on Multimedia 2022 • Zhe Xue, Junping Du, Hai Zhu, Zhongchao Guan, Yunfei Long, Yu Zang, Meiyu Liang
To address these issues, we propose a Robust Diversified Graph Contrastive Network (RDGC) for incomplete multi-view clustering, which integrates multi-view representation learning and diversified graph contrastive regularization into a unified framework.
no code implementations • 7 Oct 2022 • Jiashun Liu, Zhe Xue, Ang Li
Then the whole heterogeneous information network is transformed into a hypergraph composed of different hyperedges.
no code implementations • 7 Oct 2022 • Yuyao Zeng, Junping Du, Zhe Xue, Ang Li
KGUPN contains three main layers, which are the propagation representation layer, the contextual information layer and collaborative relation layer.
no code implementations • 6 Jul 2022 • Tianyu Zhao, Junping Du, Zhe Xue, Ang Li, Zeli Guan
Aspect-Based Sentiment Analysis (ABSA) is a fine-grained task in the field of sentiment analysis, which aims to predict the polarity of aspects.
Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +4
2 code implementations • 29 Jun 2022 • Yangxi Zhou, Junping Du, Zhe Xue, Ang Li, Zeli Guan
To address this limitation, we propose SememeWSD Synonym (SWSDS) model to assign a different vector to every sense of polysemous words with the help of word sense disambiguation (WSD) and synonym set in OpenHowNet.
no code implementations • 2 Jun 2022 • Peiyu Liu, Junping Du, Zhe Xue, Ang Li
With the rapid development of information technology, "information overload" has become the main theme that plagues people's online life.
no code implementations • 25 Apr 2022 • Jie Song, Meiyu Liang, Zhe Xue, Feifei Kou, Ang Li
There is a complex correlation among the data of scientific papers.
no code implementations • 18 Apr 2022 • Changwei Zheng, Zhe Xue, Meiyu Liang, Feifei Kou, Zeli Guan
In recent years, with the increase of social investment in scientific research, the number of research results in various fields has increased significantly.
no code implementations • 11 Apr 2022 • Runyu Yu, Zhe Xue, Ang Li
In recent years, with the continuous progress of science and technology, the number of scientific research achievements is increasing day by day, as the exchange platform and medium of scientific research achievements, the scientific and technological academic conferences have become more and more abundant.
no code implementations • 11 Apr 2022 • Yang Jiang, Zhe Xue, Ang Li
Since the era of big data, the Internet has been flooded with all kinds of information.
no code implementations • 11 Apr 2022 • Yue Wang, Zhe Xue, Ang Li
With the advent of the cloud computing era, the cost of creating, capturing and managing information has gradually decreased.
no code implementations • 31 Mar 2022 • Jie Song, Meiyu Liang, Zhe Xue, Junping Du, Kou Feifei
in the heterogeneous graph of scientific papers.
no code implementations • 30 Mar 2022 • Changwei Zheng, Zhe Xue, Meiyu Liang, Feifei Kou
To simultaneously capture the spatial dependencies and temporal changes between research topics, we propose a deep neural network-based research topic hotness prediction algorithm, a spatiotemporal convolutional network model.
no code implementations • 21 Mar 2022 • Yang Jiang, Zhe Xue, Ang Li
In the era of big data, it is possible to carry out cooperative research on the research results of researchers through papers, patents and other data, so as to study the role of researchers, and produce results in the analysis of results.
no code implementations • 16 Mar 2022 • Ang Li, Junping Du, Feifei Kou, Zhe Xue, Xin Xu, Mingying Xu, Yang Jiang
In light of this, we propose a scientific and technological information oriented Semantics-adversarial and Media-adversarial Cross-media Retrieval method (SMCR) to find an effective common subspace.
no code implementations • 13 Jun 2021 • Weichuan Zhang, Xuefang Liu, Zhe Xue, Yongsheng Gao, Changming Sun
Metric-based few-shot fine-grained image classification (FSFGIC) aims to learn a transferable feature embedding network by estimating the similarities between query images and support classes from very few examples.