Re-se-arch
Our re-se-arch has been generously supported by ARO, NSF, ARFL, IARPA, BlueHalo and Salesforce.
2017
Zhao, Bo; Wu, Botong; Wu, Tianfu; Wang, Yizhou
Zero-Shot Learning Posed as a Missing Data Problem Workshop
2017 IEEE International Conference on Computer Vision Workshops, ICCV Workshops 2017, Venice, Italy, October 22-29, 2017, 2017.
@workshop{Zhao_ZeroShot,
title = {Zero-Shot Learning Posed as a Missing Data Problem},
author = {Bo Zhao and Botong Wu and Tianfu Wu and Yizhou Wang},
url = {https://arxiv.org/abs/1612.00560},
doi = {10.1109/ICCVW.2017.310},
year = {2017},
date = {2017-01-01},
booktitle = {2017 IEEE International Conference on Computer Vision Workshops,
ICCV Workshops 2017, Venice, Italy, October 22-29, 2017},
pages = {2616--2622},
abstract = {This paper presents a method of zero-shot learning (ZSL) which poses ZSL as the missing data problem, rather than the missing label problem. While most popular methods in ZSL focus on learning the mapping function from the image feature space to the label embedding space, the proposed method explores a simple yet effective transductive framework in the reverse mapping. Our method estimates data distribution of unseen classes in the image feature space by transferring knowledge from the label embedding space. It assumes that data of each seen and unseen class follow Gaussian distribution in the image feature space and utilizes Gaussian mixture model to model data. The signature is introduced to describe the data distribution of each class. In experiments, our method obtains 87.38% and 61.08% mean accuracies on the Animals with Attributes (AwA) and the Caltech-UCSD Birds-200-2011 (CUB) datasets respectively, which outperforms the runner-up methods significantly by 4.95% and 6.38%. In addition, we also investigate the extension of our method to open-set classification.},
howpublished = {arXiv preprint},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
This paper presents a method of zero-shot learning (ZSL) which poses ZSL as the missing data problem, rather than the missing label problem. While most popular methods in ZSL focus on learning the mapping function from the image feature space to the label embedding space, the proposed method explores a simple yet effective transductive framework in the reverse mapping. Our method estimates data distribution of unseen classes in the image feature space by transferring knowledge from the label embedding space. It assumes that data of each seen and unseen class follow Gaussian distribution in the image feature space and utilizes Gaussian mixture model to model data. The signature is introduced to describe the data distribution of each class. In experiments, our method obtains 87.38% and 61.08% mean accuracies on the Animals with Attributes (AwA) and the Caltech-UCSD Birds-200-2011 (CUB) datasets respectively, which outperforms the runner-up methods significantly by 4.95% and 6.38%. In addition, we also investigate the extension of our method to open-set classification.