Name
Abstract | ||
|---|---|---|
The Active Sensing Testbed (AST) is a novel framework for research in machine perception and world-view reasoning. The AST supports exploratory development of perception systems that can build internal models of the world by combining multi-view and multi-modal analytics, utilize these models to form hypotheses about a scene, and potentially take action to fill in gaps in knowledge or make predictions about future world states. As a modular software framework, the AST is intended to lower the barrier to entry for researchers and developers in applying state-of-the-art computer vision techniques to real-world problems. |
Year | Venue | DocType |
|---|---|---|
2021 | THIRTY-FIFTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, THIRTY-THIRD CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE AND THE ELEVENTH SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE | Conference |
Volume | ISSN | Citations |
35 | 2159-5399 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lee Stearns | 1 | 0 | 0.34 |
| Neil Fendley | 2 | 2 | 0.73 |
| Ashley J. Llorens | 3 | 4 | 1.84 |