Name
Abstract | ||
|---|---|---|
Massive user generated content (UGC) videos are produced each day on the Internet. These videos have become a very important integrant in existing social networking services (SNS). However, unlike professional films, the content of UGC videos is usually unstructured and lacks contextual annotation for management. The motivation behind Huawei Accurate and Fast Mobile Video Annotation Challenge (MoVAC) is to evaluate different algorithms on the generation of local annotation on UGC videos under the same protocol, and to compare them not only in accuracy but also in efficiency. More than 15 teams from different countries have enrolled in this competition, and in the final round 17 submissions with valid result from 6 teams were received. The results show that recent popular deep convolutional neural networks (CNN) could be a potentially good solution to this task. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICMEW.2014.6890607 | ICME Workshops |
Keywords | Field | DocType |
ugc,video signal processing,ugc videos,local annotation generation,video annotation,convolution,cnn,movac,deep learning,huawei accurate and fast mobile video annotation challenge,internet,social networking services,sns,user generated content videos,neural nets,protocol,deep convolutional neural networks,feature extraction,support vector machines,databases,testing,accuracy | User-generated content,Computer vision,Annotation,Social network,Convolutional neural network,Computer science,Support vector machine,Feature extraction,Artificial intelligence,Deep learning,Multimedia,The Internet | Conference |
ISSN | Citations | PageRank |
1945-7871 | 0 | 0.34 |
References | Authors | |
13 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhenhua Chai | 1 | 12 | 6.59 |
| Dong Wang | 2 | 1351 | 186.07 |
| Tian Wang | 3 | 0 | 0.34 |
| Jianzhuang Liu | 4 | 1614 | 98.72 |
| Xinzi Zhang | 5 | 0 | 0.34 |
| yihong gong | 6 | 7300 | 470.57 |