Name
Abstract | ||
|---|---|---|
Image caption is one of the hottest research topics at the moment in the image processing field. However, most image caption models based on the Encoder-Decoder framework cannot accurately find the alignment relationship between objects in the image and objects in the text, resulting in an inaccurate description. In this work, we propose a denoising framework in which the image and the text are processed separately, and we use the caption stem to find the alignment relationship between image and text accurately. Compared with previous work, this framework can more accurately find the alignment of images and texts and generate more accurate captions. Experiments on the MSCOCO dataset show that the caption generated by this model can express the content of an image more accurately. Besides, the model can generate short captions and long captions with rich semantic according to users' needs. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/DASC/PiCom/CBDCom/CyberSciTech.2019.00151 | IEEE 17TH INT CONF ON DEPENDABLE, AUTONOM AND SECURE COMP / IEEE 17TH INT CONF ON PERVAS INTELLIGENCE AND COMP / IEEE 5TH INT CONF ON CLOUD AND BIG DATA COMP / IEEE 4TH CYBER SCIENCE AND TECHNOLOGY CONGRESS (DASC/PICOM/CBDCOM/CYBERSCITECH) |
Keywords | Field | DocType |
image caption, object detection, syntax analysis, attention | Noise reduction,Computer vision,Object detection,Computer science,Image processing,Feature extraction,Artificial intelligence,Image denoising | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yulong Zhang | 1 | 0 | 0.34 |
| Yuxin Ding | 2 | 237 | 21.52 |
| Rui Wu | 3 | 9 | 5.26 |
| Fuxing Xue | 4 | 0 | 0.34 |