Joint Hierarchical Learning for Efficient Multi-Class Object Detection
Abstract
In addition to multi-class classification, the multi-class object detection task consists further in classifying a dominating background label. In this work, we present a novel approach where relevant classes are ranked higher and background labels are rejected. To this end, we arrange the classes into a tree structure where the classifiers are trained in a joint framework combining ranking and classification constraints. Our convex problem formulation naturally allows to apply a tree traversal algorithm that searches for the best class label and progressively rejects background labels. We evaluate our approach on the PASCAL VOC 2007 dataset and show a considerable speed-up of the detection time with increased detection performance.
Cite
Text
Fard et al. "Joint Hierarchical Learning for Efficient Multi-Class Object Detection." IEEE/CVF Winter Conference on Applications of Computer Vision, 2014. doi:10.1109/WACV.2014.6836090Markdown
[Fard et al. "Joint Hierarchical Learning for Efficient Multi-Class Object Detection." IEEE/CVF Winter Conference on Applications of Computer Vision, 2014.](https://mlanthology.org/wacv/2014/fard2014wacv-joint/) doi:10.1109/WACV.2014.6836090BibTeX
@inproceedings{fard2014wacv-joint,
title = {{Joint Hierarchical Learning for Efficient Multi-Class Object Detection}},
author = {Fard, Hamidreza Odabai and Chaouch, Mohamed and Pham, Quoc Cuong and Vacavant, Antoine and Chateau, Thierry},
booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision},
year = {2014},
pages = {261-268},
doi = {10.1109/WACV.2014.6836090},
url = {https://mlanthology.org/wacv/2014/fard2014wacv-joint/}
}