Name
Title | ||
|---|---|---|
DeepID-Net: Object Detection with Deformable Part Based Convolutional Neural Networks. |
Abstract | ||
|---|---|---|
In this paper, we propose deformable deep convolutional neural networks for generic object detection. This new deep learning object detection framework has innovations in multiple aspects. In the proposed new deep architecture, a new deformation constrained pooling (def-pooling) layer models the deformation of object parts with geometric constraint and penalty. A new pre-training strategy is propo... |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/TPAMI.2016.2587642 | IEEE Transactions on Pattern Analysis and Machine Intelligence |
Keywords | Field | DocType |
Object detection,Context modeling,Deformable models,Machine learning,Visualization,Training,Neural networks | Object detection,Computer vision,Pattern recognition,Computer science,Visualization,Convolutional neural network,Deep belief network,Context model,Artificial intelligence,Deep learning,Artificial neural network,Test set | Journal |
Volume | Issue | ISSN |
39 | 7 | 0162-8828 |
Citations | PageRank | References |
15 | 0.62 | 52 |
Authors | ||
14 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wanli Ouyang | 1 | 2371 | 105.17 |
| Xingyu Zeng | 2 | 327 | 16.08 |
| Xiaogang Wang | 3 | 9647 | 386.70 |
| Shi Qiu | 4 | 250 | 29.03 |
| Ping Luo | 5 | 2540 | 111.68 |
| Yonglong Tian | 6 | 300 | 15.84 |
| Hongsheng Li | 7 | 1516 | 85.29 |
| Shuo Yang | 8 | 330 | 28.54 |
| Zhe Wang | 9 | 199 | 19.26 |
| hongyang li | 10 | 211 | 9.33 |
| Kun Wang | 11 | 150 | 45.41 |
| Junjie Yan | 12 | 1288 | 58.19 |
| Chen Change Loy | 13 | 4484 | 178.56 |
| Xiaoou Tang | 14 | 15728 | 670.19 |