Name
Abstract | ||
|---|---|---|
While state-of-the-art general object detectors are getting better and better, there are not many systems specifically designed to take advantage of the instance detection problem. For many applications, such as household robotics, a system may need to recognize a few very specific instances at a time. Speed can be critical in these applications, as can the need to recognize previously unseen instances. We introduce a Target Driven Instance Detector(TDID), which modifies existing general object detectors for the instance recognition setting. TDID not only improves performance on instances seen during training, with a fast runtime, but is also able to generalize to detect novel instances. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Computer Vision and Pattern Recognition | Computer science,Artificial intelligence,Detector,Machine learning,Robotics |
DocType | Volume | Citations |
Journal | abs/1803.04610 | 0 |
PageRank | References | Authors |
0.34 | 25 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Phil Ammirato | 1 | 16 | 0.94 |
| Cheng-Yang Fu | 2 | 138 | 6.57 |
| Mykhailo Shvets | 3 | 10 | 1.54 |
| Jana Kosecká | 4 | 1523 | 129.85 |
| Alexander C. Berg | 5 | 10554 | 630.24 |