Model Recommendation: Generating Object Detectors From Few Samples

Yu-Xiong Wang, Martial Hebert; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 1619-1628

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.

Related Material


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[bibtex]
@InProceedings{Wang_2015_CVPR,
author = {Wang, Yu-Xiong and Hebert, Martial},
title = {Model Recommendation: Generating Object Detectors From Few Samples},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2015}
}