Relative Attribute Learning with Deep Attentive Cross-image Representation


Zeshang Zhang, Yingming Li, Zhongfei Zhang ;
Proceedings of The 10th Asian Conference on Machine Learning, PMLR 95:879-892, 2018.


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.

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