Name
Abstract | ||
|---|---|---|
Various perceptual domains have underlying compositional semantics that are rarely captured in current models. We suspect this is because directly learning the compositional structure has evaded these models. Yet, the compositional structure of a given domain can be grounded in a separate domain thereby simplifying its learning. To that end, we propose a new approach to modeling bimodal perceptual... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TPAMI.2017.2693987 | IEEE Transactions on Pattern Analysis and Machine Intelligence |
Keywords | Field | DocType |
Dictionaries,Encoding,Semantics,Poles and towers,Robot sensing systems,Visualization | Robot learning,Principle of compositionality,Computer vision,Colored,Pattern recognition,Computer science,Visualization,Sparse approximation,Artificial intelligence,Perception,Semantics,Encoding (memory) | Journal |
Volume | Issue | ISSN |
40 | 5 | 0162-8828 |
Citations | PageRank | References |
0 | 0.34 | 19 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Suren Kumar | 1 | 35 | 4.78 |
| Vikas Dhiman | 2 | 7 | 1.80 |
| Parker A. Koch | 3 | 0 | 0.34 |
| Jason J. Corso | 4 | 37 | 3.84 |