Name
Abstract | ||
|---|---|---|
In real-world recognition/classification tasks, limited by various objective factors, it is usually difficult to collect training samples to exhaust all classes when training a recognizer or classifier. A more realistic scenario is open set recognition (OSR), where incomplete knowledge of the world exists at training time, and unknown classes can be submitted to an algorithm during testing, requir... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TPAMI.2020.2981604 | IEEE Transactions on Pattern Analysis and Machine Intelligence |
Keywords | DocType | Volume |
Training,Testing,Task analysis,Semantics,Face recognition,Data visualization | Journal | 43 |
Issue | ISSN | Citations |
10 | 0162-8828 | 12 |
PageRank | References | Authors |
0.63 | 35 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chuanxing Geng | 1 | 18 | 1.76 |
| Sheng-Jun Huang | 2 | 475 | 27.21 |
| Songcan Chen | 3 | 4148 | 191.89 |