Proceedings of The 10th Asian Conference on Machine Learning, PMLR 95:879-892, 2018.
Abstract
In this paper, we study the relative attribute learning problem, which refers to comparing the strengths of a specific attribute between image pairs, with a new perspective of cross-image representation learning. In particular, we introduce a deep attentive cross-image representation learning (DACRL) model, which first extracts single-image representation with one shared subnetwork, and then learns attentive cross-image representation through considering the channel-wise attention of concatenated single-image feature maps. Taking a pair of images as input, DACRL outputs a posterior probability indicating whether the first image in the pair has a stronger presence of attribute than the second image. The whole network is jointly optimized via a unified end-to-end deep learning scheme. Extensive experiments on several benchmark datasets demonstrate the effectiveness of our approach against the state-of-the-art methods.
@InProceedings{pmlr-v95-zhang18d,
title = {Relative Attribute Learning with Deep Attentive Cross-image Representation},
author = {Zhang, Zeshang and Li, Yingming and Zhang, Zhongfei},
booktitle = {Proceedings of The 10th Asian Conference on Machine Learning},
pages = {879--892},
year = {2018},
editor = {Zhu, Jun and Takeuchi, Ichiro},
volume = {95},
series = {Proceedings of Machine Learning Research},
address = {},
month = {14--16 Nov},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v95/zhang18d/zhang18d.pdf},
url = {http://proceedings.mlr.press/v95/zhang18d.html},
abstract = {In this paper, we study the relative attribute learning problem, which refers to comparing the strengths of a specific attribute between image pairs, with a new perspective of cross-image representation learning. In particular, we introduce a deep attentive cross-image representation learning (DACRL) model, which first extracts single-image representation with one shared subnetwork, and then learns attentive cross-image representation through considering the channel-wise attention of concatenated single-image feature maps. Taking a pair of images as input, DACRL outputs a posterior probability indicating whether the first image in the pair has a stronger presence of attribute than the second image. The whole network is jointly optimized via a unified end-to-end deep learning scheme. Extensive experiments on several benchmark datasets demonstrate the effectiveness of our approach against the state-of-the-art methods.}
}
%0 Conference Paper
%T Relative Attribute Learning with Deep Attentive Cross-image Representation
%A Zeshang Zhang
%A Yingming Li
%A Zhongfei Zhang
%B Proceedings of The 10th Asian Conference on Machine Learning
%C Proceedings of Machine Learning Research
%D 2018
%E Jun Zhu
%E Ichiro Takeuchi
%F pmlr-v95-zhang18d
%I PMLR
%J Proceedings of Machine Learning Research
%P 879--892
%U http://proceedings.mlr.press
%V 95
%W PMLR
%X In this paper, we study the relative attribute learning problem, which refers to comparing the strengths of a specific attribute between image pairs, with a new perspective of cross-image representation learning. In particular, we introduce a deep attentive cross-image representation learning (DACRL) model, which first extracts single-image representation with one shared subnetwork, and then learns attentive cross-image representation through considering the channel-wise attention of concatenated single-image feature maps. Taking a pair of images as input, DACRL outputs a posterior probability indicating whether the first image in the pair has a stronger presence of attribute than the second image. The whole network is jointly optimized via a unified end-to-end deep learning scheme. Extensive experiments on several benchmark datasets demonstrate the effectiveness of our approach against the state-of-the-art methods.
Zhang, Z., Li, Y. & Zhang, Z.. (2018). Relative Attribute Learning with Deep Attentive Cross-image Representation. Proceedings of The 10th Asian Conference on Machine Learning, in PMLR 95:879-892
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