Name
Title | ||
|---|---|---|
Multi-modal space structure: a new kind of latent correlation for multi-modal entity resolution. |
Abstract | ||
|---|---|---|
Multi-modal data is becoming more common than before because of big data issues. Finding the semantically equal or similar objects from different data sources(called entity resolution) is one of the heart problem of multi-modal task. Current models for solving this problem usually needs much paired data to find the latent correlation between multi-modal data, which is of high cost. A new kind latent correlation is proposed in this article. With the correlation, multi-modal objects can be uniformly represented in a commonly shard space. A classifying based model is designed for multi-modal entity resolution task. With the proposed method, the demand of training data can be decreased much. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Artificial Intelligence | Training set,Name resolution,Pattern recognition,Computer science,Heart problem,Shard,Correlation,Artificial intelligence,Paired Data,Big data,Modal |
DocType | Volume | Citations |
Journal | abs/1804.08010 | 0 |
PageRank | References | Authors |
0.34 | 20 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qibin Zheng | 1 | 3 | 1.73 |
| XingChun Diao | 2 | 4 | 4.43 |
| Jianjun Cao | 3 | 1 | 6.79 |
| Xiaolei Zhou | 4 | 2 | 1.72 |
| Yi Liu | 5 | 45 | 33.47 |
| Hongmei Li | 6 | 3 | 1.39 |