Home/Model Recommendation: Generating Object Detectors From Few Samples

Model Recommendation: Generating Object Detectors From Few Samples

Yuxiong Wang and Martial Hebert
Conference Paper, Carnegie Mellon University, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), June, 2015

Download Publication (PDF)

Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author’s copyright. These works may not be reposted without the explicit permission of the copyright holder.

Abstract

n this paper, we explore an approach to generating detectors that is radically different from the conventional way of learning a detector from a large corpus of annotated positive and negative data samples. Instead, we assume that we have evaluated ”off-line” a large library of detectors against a large set of detection tasks. Given a new target task, we evaluate a subset of the models on few samples from the new task and we use the matrix of models-tasks ratings to predict the performance of all the models in the library on the new task, enabling us to select a good set of detectors for the new task. This approach has three key advantages of great interest in practice: 1) generating a large collection of expressive models in an unsupervised manner is possible; 2) a far smaller set of annotated samples is needed compared to that required for training from scratch; and 3) recommending models is a very fast operation compared to the notoriously expensive training procedures of modern detectors. (1) will make the models informative across different categories; (2) will dramatically reduce the need for manually annotating vast datasets for training detectors; and (3) will enable rapid generation of new detectors.

BibTeX Reference
@conference{Wang-2015-5975,
title = {Model Recommendation: Generating Object Detectors From Few Samples},
author = {Yuxiong Wang and Martial Hebert},
booktitle = {IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR)},
school = {Robotics Institute , Carnegie Mellon University},
month = {June},
year = {2015},
address = {Pittsburgh, PA},
}
2017-09-13T10:38:40+00:00