NDLI: Predicting Multiple Attributes via Relative Multi-task Learning

Content Provider	IEEE Xplore Digital Library
Author	Lin Chen Qiang Zhang Baoxin Li
Copyright Year	2014
Description	Author affiliation: Arizona State Univ., Tempe, AZ, USA (Lin Chen; Qiang Zhang; Baoxin Li)
Abstract	Relative attributes learning aims to learn ranking functions describing the relative strength of attributes. Most of current learning approaches learn ranking functions for each attribute independently without considering possible intrinsic relatedness among the attributes. For a problem involving multiple attributes, it is reasonable to assume that utilizing such relatedness among the attributes would benefit learning, especially when the number of labeled training pairs are very limited. In this paper, we proposed a relative multi-attribute learning framework that integrates relative attributes into a multi-task learning scheme. The formulation allows us to exploit the advantages of the state-of-the-art regularization-based multi-task learning for improved attribute learning. In particular, using joint feature learning as the case studies, we evaluated our framework with both synthetic data and two real datasets. Experimental results suggest that the proposed framework has clear performance gain in ranking accuracy and zero-shot learning accuracy over existing methods of independent relative attributes learning and multi-task learning.
Starting Page	1027
Ending Page	1034
File Size	334492
Page Count	8
File Format	PDF
ISBN	9781479951185
ISSN	10636919
DOI	10.1109/CVPR.2014.135
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-06-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Correlation Joints Vectors Training Accuracy Cost function
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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