Graph kernelsGraph classificationSocial networksBioinformaticsGraph kernels have proven to be a promising approach for tackling the graph similarity and learning tasks at the same time. Most graph kernels are instances of the R-convolution framework. These kernels decompose graphs into their substructures ...
Similarities: a toolkit for similarity calculation and semantic search. 相似度计算、匹配搜索工具包,支持亿级数据文搜文、文搜图、图搜图,python3开发,开箱即用。 nlpsearch-enginedeep-learningmatchingpytorchsimilarityimage-searchbm25text-matchingsimilarity-searchimage-similarityfaiss ...
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entities and edges are deduplicated, and new edges are built across multiple data sources. For example, the same restaurant on Foursquare and internal data should be represented as the same node. This connects disparate restaurant data from multiple sources via the same node—looking up a restaur...
In CGMN, graphs are used for both visual and textual representation to achieve intra-relation reasoning across regions and words, respectively. Furthermore, we propose a novel graph node matching loss to learn fine-grained cross-modal correspondence and to achieve inter-relation reasoning. ...
Graph Structured Network for Image-Text Matching (CVPR 2020) 模型介绍:GSMN则用了更复杂的方式对齐图文。它首先分别构造了图像和文本的关系图,然后基于两个关系图进行node level和structure level的匹配。 3、引入外部知识 Knowledge Aware Semantic Concept Expansion for Image-Text Matching (IJCAI 2019) 模型介绍...
similarities of all pairs of image regions and words in sentence, we consider learning embeddings for images and textswhich independently project the two heterogeneousdata modalities into a joint space.Thus, the similarity between image and text can bedirected compared on the learned embeddings. ...
In example embodiments, similarity is determined based on graph representations of the incidents in which security events are represented as nodes, using graph matching techniques or incident thumbprints computed from node embeddings. The identified similar incidents can provide context to inform threat ...
Graph kernelsGraph classificationSocial networksBioinformaticsGraph kernels have proven to be a promising approach for tackling the graph similarity and learning tasks at the same time. Most graph kernels are instances of the R-convolution framework. These kernels decompose graphs into their substructures ...
The second phase for each layer graph, mapping the vertex to feature vector (Vertex Embedding), improves the proposed model. To reduce the node-embedding size to be efficient with the KD-tree, indexing a dimension reduction, the principal component analysis (PCA) method is used. Furthermore, ...