Name
Abstract | ||
|---|---|---|
We show quantitative evidence that a full relational model of the body performs better at upper body parsing than the standard tree model, despite the need to adopt approximate inference and learning procedures. Our method uses an approximate search for inference, and an approximate structure learning method to learn. We compare our method to state of the art methods on our dataset (which depicts a wide range of poses), on the standard Buffy dataset, and on the reduced PASCAL dataset published recently. Our results suggest that the Buffy dataset over emphasizes poses where the arms hang down, and that leads to generalization problems. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/978-3-642-15561-1_17 | ECCV (4) |
Keywords | Field | DocType |
standard tree model,standard buffy dataset,upper body,approximate search,reduced pascal dataset,buffy dataset,full relational model,approximate inference,art method,improved human parsing,approximate structure,relational model | Computer science,Inference,Structure learning,Decision tree model,Approximate inference,Active appearance model,Hang,Artificial intelligence,Parsing,Relational model,Machine learning | Conference |
Volume | ISSN | ISBN |
6314 | 0302-9743 | 3-642-15560-X |
Citations | PageRank | References |
60 | 3.22 | 24 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Duan Tran | 1 | 292 | 16.10 |
| D. A. Forsyth | 2 | 9227 | 1138.80 |