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Spectral attribute learning for visual regression
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DOI：10.1016/j.patcog.2017.01.009.png)
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Abstract
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A number of computer vision problems such as facial age estimation, crowd counting and pose estimation can be solved by learning regression mapping on low-level imagery features. We show that visual regression can be substantially improved by two-stage regression where imagery features are first mapped to an attribute space which explicitly models latent correlations across continuously-changing output. We propose an approach to automatically discover spectral attributes which avoids manual work required for defining hand-crafted attribute representations. Visual attribute regression outperforms direct visual regression and our spectral attribute visual regression achieves state-of-the-art accuracy in multiple applications.
Keywords:
Facial age estimation
Crowd counting
Head pose estimation
Spectral learning
Attributes
Regression
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