Name
Title | ||
|---|---|---|
Training Multi-Object Detector by Estimating Bounding Box Distribution for Input Image. |
Abstract | ||
|---|---|---|
In multi-object detection using neural networks, the fundamental problem is,"How should the network learn a variable number of bounding boxes in different input images?". Previous methods train a multi-object detection network through a procedure that directly assigns the ground truth bounding boxes to the specific locations of the network's output. However, this procedure makes the training of a multi-object detection network too heuristic and complicated. In this paper, we reformulate the multi-object detection task as a problem of density estimation of bounding boxes. Instead of assigning each ground truth to specific locations of network's output, we train a network by estimating the probability density of bounding boxes in an input image using a mixture model. For this purpose, we propose a novel network for object detection called Mixture Density Object Detector (MDOD), and the corresponding objective function for the density-estimation-based training. We applied MDOD to MS COCO dataset. Our proposed method not only deals with multi-object detection problems in a new approach, but also improves detection performances through MDOD. The code is available: https://github.com/yoojy31/MDOD. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ICCV48922.2021.00342 | ICCV |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jaeyoung Yoo | 1 | 1 | 3.08 |
| Hojun Lee | 2 | 0 | 0.34 |
| Inseop Chung | 3 | 1 | 1.36 |
| Geonseok Seo | 4 | 0 | 0.34 |
| Nojun Kwak | 5 | 862 | 63.79 |