Learning attribute relation in attribute-based zero-shot classification

Authors:
Mingxia Liu;Songcan Chen;Daoqiang Zhang
Affiliations:
School of Computer Science and Engineering, Nanjing University of Aeronautics & Astronautics, Nanjing, P.R. China,School of Information Science and Technology, Taishan University, Taian, P.R. ...;School of Computer Science and Engineering, Nanjing University of Aeronautics & Astronautics, Nanjing, P.R. China;School of Computer Science and Engineering, Nanjing University of Aeronautics & Astronautics, Nanjing, P.R. China
Venue:
IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Year:
2012

Citing 10
Cited 0

SMO algorithm for least-squares SVM formulations

Neural Computation
Benchmarking Least Squares Support Vector Machine Classifiers

Machine Learning
Pattern Recognition and Machine Learning (Information Science and Statistics)

Pattern Recognition and Machine Learning (Information Science and Statistics)
Convex multi-task feature learning

Machine Learning
Zero-data learning of new tasks

AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Interactively building a discriminative vocabulary of nameable attributes

CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Evaluating knowledge transfer and zero-shot learning in a large-scale setting

CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Image ranking and retrieval based on multi-attribute queries

CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Classifying covert photographs

CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Actively selecting annotations among objects and attributes

ICCV '11 Proceedings of the 2011 International Conference on Computer Vision

Quantified Score

Hi-index	0.00

Visualization

Abstract

Recently, zero-shot learning has attracted increasing attention in computer vision community. One way of realizing zero-shot learning is by resorting to knowledge about attributes and object categories. Most existing attribute-centric approaches focus on attribute-class relation artificially derived by linguistic knowledge base or mutual information. In this paper, we aim to learn the attribute-attribute relation automatically and explicitly. Specifically, we propose to incorporate the attribute relation learning into attribute classifier design in a unified framework. Furthermore, we develop a new scheme for attribute-based zero-shot object classification, such that the learned attribute relation can be reused to boost the traditional attribute classifiers. Extensive experimental results demonstrate that our proposed method can enhance the performance of attribute prediction and zero-shot learning.