Model Recommendation: Generating Object Detectors From Few Samples - Robotics Institute Carnegie Mellon University

Model Recommendation: Generating Object Detectors From Few Samples

Conference Paper, Proceedings of (CVPR) Computer Vision and Pattern Recognition, pp. 1619 - 1628, June, 2015

Abstract

In 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

@conference{Wang-2015-5975,
author = {Yuxiong Wang and Martial Hebert},
title = {Model Recommendation: Generating Object Detectors From Few Samples},
booktitle = {Proceedings of (CVPR) Computer Vision and Pattern Recognition},
year = {2015},
month = {June},
pages = {1619 - 1628},
}