Name
Abstract | ||
|---|---|---|
We propose a new attention model for video question answering. The main idea of the attention models is to locate on the most informative parts of the visual data. The attention mechanisms are quite popular these days. However, most existing visual attention mechanisms regard the question as a whole. They ignore the word-level semantics where each word can have different attentions and some words ... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TIP.2018.2859820 | IEEE Transactions on Image Processing |
Keywords | Field | DocType |
Knowledge discovery,Visualization,Task analysis,Computational modeling,Syntactics,Natural languages,Semantics | Question answering,Pattern recognition,Natural language,Natural language processing,Artificial intelligence,Knowledge extraction,Parsing,Syntax,Semantics,Mathematics,Recursion,Visual Word | Journal |
Volume | Issue | ISSN |
27 | 11 | 1057-7149 |
Citations | PageRank | References |
6 | 0.44 | 17 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hongyang Xue | 1 | 81 | 3.54 |
| Wenqing Chu | 2 | 46 | 6.74 |
| Zhou Zhao | 3 | 773 | 90.87 |
| Deng Cai | 4 | 7938 | 320.26 |