Name
Abstract | ||
|---|---|---|
How do we build a general and broad object detection system? We use all labels of all concepts ever annotated. These labels span diverse datasets with potentially inconsistent taxonomies. In this paper, we present a simple method for training a unified detector on multiple large-scale datasets. We use dataset-specific training protocols and losses, but share a common detection architecture with dataset-specific outputs. We show how to automatically integrate these dataset-specific outputs into a common semantic taxonomy. In contrast to prior work, our approach does not require manual taxonomy reconciliation. Our multi-dataset detector performs as well as dataset-specific models on each training domain, but generalizes much better to new unseen domains. Entries based on the presented methodology ranked first in the object detection and instance segmentation tracks of the ECCV 2020 Robust Vision Challenge. |
Year | Venue | DocType |
|---|---|---|
2022 | IEEE Conference on Computer Vision and Pattern Recognition | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xingyi Zhou | 1 | 140 | 5.96 |
| Vladlen Koltun | 2 | 4064 | 162.63 |
| Philipp Krähenbühl | 3 | 1909 | 64.06 |