Name
Title | ||
|---|---|---|
Collaborative Active Visual Recognition from Crowds: A Distributed Ensemble Approach. |
Abstract | ||
|---|---|---|
Active learning is an effective way of engaging users to interactively train models for visual recognition more efficiently. The vast majority of previous works focused on active learning with a single human oracle. The problem of active learning with multiple oracles in a collaborative setting has not been well explored. We present a collaborative computational model for active learning with mult... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TPAMI.2017.2682082 | IEEE Transactions on Pattern Analysis and Machine Intelligence |
Keywords | Field | DocType |
Visualization,Collaboration,Labeling,Noise measurement,Data models,Crowdsourcing,Kernel | Data modeling,Crowds,Collaborative learning,Active learning,Pattern recognition,Visualization,Computer science,Crowdsourcing,Oracle,Artificial intelligence,Kernel method,Machine learning | Journal |
Volume | Issue | ISSN |
40 | 3 | 0162-8828 |
Citations | PageRank | References |
3 | 0.38 | 33 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gang Hua | 1 | 2796 | 157.90 |
| Chengjiang Long | 2 | 107 | 14.21 |
| Ming Yang | 3 | 3471 | 162.50 |
| Yan Gao | 4 | 197 | 10.54 |