Tropel: Crowdsourcing Detectors with Minimal Training

Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt

Standard

Tropel: Crowdsourcing Detectors with Minimal Training. / Belongie, Serge; Patterson, Genevieve; Perona, Pietro; Hays, James.

Proceedings of the AAAI Conference on Human Computation and Crowdsourcing. Bind 3 1. udg. 2015. s. 150-159.

Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt

Harvard

Belongie, S, Patterson, G, Perona, P & Hays, J 2015, Tropel: Crowdsourcing Detectors with Minimal Training. i Proceedings of the AAAI Conference on Human Computation and Crowdsourcing. 1 udg, bind 3, s. 150-159. <https://ojs.aaai.org/index.php/HCOMP/article/view/13224>

APA

Belongie, S., Patterson, G., Perona, P., & Hays, J. (2015). Tropel: Crowdsourcing Detectors with Minimal Training. I Proceedings of the AAAI Conference on Human Computation and Crowdsourcing (1 udg., Bind 3, s. 150-159) https://ojs.aaai.org/index.php/HCOMP/article/view/13224

Vancouver

Belongie S, Patterson G, Perona P, Hays J. Tropel: Crowdsourcing Detectors with Minimal Training. I Proceedings of the AAAI Conference on Human Computation and Crowdsourcing. 1 udg. Bind 3. 2015. s. 150-159

Author

Belongie, Serge ; Patterson, Genevieve ; Perona, Pietro ; Hays, James. / Tropel: Crowdsourcing Detectors with Minimal Training. Proceedings of the AAAI Conference on Human Computation and Crowdsourcing. Bind 3 1. udg. 2015. s. 150-159

Bibtex

@inproceedings{20f1a90d476d44f3a062f501c8975bb0,
title = "Tropel: Crowdsourcing Detectors with Minimal Training",
abstract = "This paper introduces the Tropel system which enables non-technical users to create arbitrary visual detectors without first annotating a training set. Our primary contribution is a crowd active learning pipeline that is seeded with only a single positive example and an unlabeled set of training images. We examine the crowd's ability to train visual detectors given severely limited training themselves. This paper presents a series of experiments that reveal the relationship between worker training, worker consensus and the average precision of detectors trained by crowd-in-the-loop active learning. In order to verify the efficacy of our system, we train detectors for bird species that work nearly as well as those trained on the exhaustively labeled CUB 200 dataset at significantly lower cost and with little effort from the end user. To further illustrate the usefulness of our pipeline, we demonstrate qualitative results on unlabeled datasets containing fashion images and street-level photographs of Paris.",
author = "Serge Belongie and Genevieve Patterson and Pietro Perona and James Hays",
year = "2015",
month = sep,
day = "23",
language = "English",
volume = "3",
pages = "150--159",
booktitle = "Proceedings of the AAAI Conference on Human Computation and Crowdsourcing",
edition = "1",

}

RIS

TY - GEN

T1 - Tropel: Crowdsourcing Detectors with Minimal Training

AU - Belongie, Serge

AU - Patterson, Genevieve

AU - Perona, Pietro

AU - Hays, James

PY - 2015/9/23

Y1 - 2015/9/23

N2 - This paper introduces the Tropel system which enables non-technical users to create arbitrary visual detectors without first annotating a training set. Our primary contribution is a crowd active learning pipeline that is seeded with only a single positive example and an unlabeled set of training images. We examine the crowd's ability to train visual detectors given severely limited training themselves. This paper presents a series of experiments that reveal the relationship between worker training, worker consensus and the average precision of detectors trained by crowd-in-the-loop active learning. In order to verify the efficacy of our system, we train detectors for bird species that work nearly as well as those trained on the exhaustively labeled CUB 200 dataset at significantly lower cost and with little effort from the end user. To further illustrate the usefulness of our pipeline, we demonstrate qualitative results on unlabeled datasets containing fashion images and street-level photographs of Paris.

AB - This paper introduces the Tropel system which enables non-technical users to create arbitrary visual detectors without first annotating a training set. Our primary contribution is a crowd active learning pipeline that is seeded with only a single positive example and an unlabeled set of training images. We examine the crowd's ability to train visual detectors given severely limited training themselves. This paper presents a series of experiments that reveal the relationship between worker training, worker consensus and the average precision of detectors trained by crowd-in-the-loop active learning. In order to verify the efficacy of our system, we train detectors for bird species that work nearly as well as those trained on the exhaustively labeled CUB 200 dataset at significantly lower cost and with little effort from the end user. To further illustrate the usefulness of our pipeline, we demonstrate qualitative results on unlabeled datasets containing fashion images and street-level photographs of Paris.

M3 - Article in proceedings

VL - 3

SP - 150

EP - 159

BT - Proceedings of the AAAI Conference on Human Computation and Crowdsourcing

ER -

ID: 307528886