Search Results for author:
Found 16 papers, 2 papers with code
Dynamic Self-adaptive Multiscale Distillation from Pre-trained Multimodal Large Model for Efficient Cross-modal Representation Learning
Our methodology streamlines pre-trained multimodal large models using only their output features and original image-level information, requiring minimal computational resources.
Improving Expressive Power of Spectral Graph Neural Networks with Eigenvalue Correction
Moreover, we theoretically establish that the number of distinguishable eigenvalues plays a pivotal role in determining the expressive power of spectral graph neural networks.
Topic model based on co-occurrence word networks for unbalanced short text datasets
We propose a straightforward solution for detecting scarce topics in unbalanced short-text datasets.
Federated Topic Model and Model Pruning Based on Variational Autoencoder
The first method involves slow pruning throughout the entire model training process, which has limited acceleration effect on the model training process, but can ensure that the pruned model achieves higher accuracy.
Semantic Representation Learning of Scientific Literature based on Adaptive Feature and Graph Neural Network
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.
Semantic Structure Enhanced Contrastive Adversarial Hash Network for Cross-media Representation Learning
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.
Cross-modal Search Method of Technology Video based on Adversarial Learning and Feature Fusion
The proposed method uses the framework of adversarial learning to construct a video multimodal feature fusion network and a feature mapping network as generator, a modality discrimination network as discriminator.
Robust Diversified Graph Contrastive Network for Incomplete Multi-view Clustering
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.
Unsupervised Semantic Representation Learning of Scientific Literature Based on Graph Attention Mechanism and Maximum Mutual Information
Therefore, an unsupervised semantic representation learning method of scientific literature based on graph attention mechanism and maximum mutual information (GAMMI) is proposed.
Embedding Representation of Academic Heterogeneous Information Networks Based on Federated Learning
To solve the above challenges, aiming at the data information of scientific research teams closely related to science and technology, we proposed an academic heterogeneous information network embedding representation learning method based on federated learning (FedAHE), which utilizes node attention and meta path attention mechanism to learn low-dimensional, dense and real-valued vector representations while preserving the rich topological information and meta-path-based semantic information of nodes in network.
Cross-media Scientific Research Achievements Query based on Ranking Learning
With the advent of the information age, the scale of data on the Internet is getting larger and larger, and it is full of text, images, videos, and other information.
Mining and searching association relation of scientific papers based on deep learning
There is a complex correlation among the data of scientific papers.
Research on Domain Information Mining and Theme Evolution of Scientific Papers
In recent years, with the increase of social investment in scientific research, the number of research results in various fields has increased significantly.
An unsupervised cluster-level based method for learning node representations of heterogeneous graphs in scientific papers
in the heterogeneous graph of scientific papers.
Research topic trend prediction of scientific papers based on spatial enhancement and dynamic graph convolution network
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.
Cross-Media Scientific Research Achievements Retrieval Based on Deep Language Model
Science and technology big data contain a lot of cross-media information. There are images and texts in the scientific paper. The s ingle modal search method cannot well meet the needs of scientific researchers. This paper proposes a cross-media scientific research achievements retrieval method based on deep language model (CARDL). It achieves a unified cross-media semantic representation by learning the semantic association between different modal data, and is applied to the generation of text semantic vector of scientific research achievements, and then cross-media retrieval is realized through semantic similarity matching between different modal data. Experimental results show that the proposed CARDL method achieves better cross-modal retrieval performance than existing methods.
