Name
Abstract | ||
|---|---|---|
Convolutional neural network (CNN) has achieved the state-of-the-art performance in many different visual tasks. Learned from a large-scale training data set, CNN features are much more discriminative and accurate than the handcrafted features. Moreover, CNN features are also transferable among different domains. On the other hand, traditional dictionary-based features (such as BoW and spatial pyr... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/TCSVT.2015.2511543 | IEEE Transactions on Circuits and Systems for Video Technology |
Keywords | DocType | Volume |
Dictionaries,Training,Visualization,Databases,Object oriented modeling,Neural networks,Convolutional codes | Journal | 27 |
Issue | ISSN | Citations |
6 | 1051-8215 | 26 |
PageRank | References | Authors |
0.67 | 60 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yong Xu | 1 | 339 | 31.64 |
| Xu-Yao Zhang | 2 | 347 | 30.02 |
| Shuicheng Yan | 3 | 767 | 25.71 |
| Cheng-Lin Liu | 4 | 4367 | 239.75 |