Name
Abstract | ||
|---|---|---|
To avoid the exhaustive search over locations and scales, current state-of-the-art object detection systems usually involve a crucial component generating a batch of candidate object proposals from images. In this paper, we present a simple yet effective approach for segmenting object proposals via a deep architecture of recursive neural networks (ReNNs), which hierarchically groups regions for de... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TIP.2018.2859025 | IEEE Transactions on Image Processing |
Keywords | Field | DocType |
Proposals,Deep learning,Feature extraction,Neural networks,Object detection,Object segmentation,Object recognition | Object detection,Brute-force search,Pattern recognition,Inference,Feature extraction,Greedy algorithm,Image segmentation,Artificial intelligence,Artificial neural network,Mathematics,Recursion | Journal |
Volume | Issue | ISSN |
27 | 12 | 1057-7149 |
Citations | PageRank | References |
1 | 0.36 | 23 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tianshui Chen | 1 | 190 | 12.08 |
| Liang Lin | 2 | 3007 | 151.07 |
| Xian Wu | 3 | 18 | 3.00 |
| Nong Xiao | 4 | 649 | 116.15 |
| Xiaonan Luo | 5 | 697 | 92.76 |