Name
Abstract | ||
|---|---|---|
Recent successes in visual recognition can be primarily attributed to feature representation, learning algorithms, and the ever-increasing size of labeled training data. Extensive research has been devoted to the first two, but much less attention has been paid to the third. Due to the high cost of manual data labeling, the size of recent efforts such as ImageNet is still relatively small in respect to daily applications. In this work, we mainly focus on how to automatically generate identifying image data for a given visual concept on a vast scale.With the generated image data, we can train a robust recognition model for the given concept. We evaluate the proposed webly supervised approach on the benchmark Pascal VOC 2007 dataset and the results demonstrates the superiority of our method over many other state-of-the-art methods in image data collection. |
Year | Venue | Field |
|---|---|---|
2016 | MMM | Training set,Computer vision,Data collection,Pattern recognition,Computer science,Data labeling,Visual recognition,Artificial intelligence,Machine learning,The Internet |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
24 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yazhou Yao | 1 | 86 | 16.61 |
| Jian Zhang | 2 | 1305 | 100.05 |
| Xian-Sheng Hua | 3 | 6566 | 328.17 |
| Fumin Shen | 4 | 1868 | 91.49 |
| Zhenmin Tang | 5 | 678 | 55.54 |