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Pose-aligned networks for deep attribute modeling
Pose-aligned networks for deep attribute modeling
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机译:用于深度属性建模的姿势对齐网络
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摘要
Technology is disclosed for inferring human attributes from images of people. The attributes can include, for example, gender, age, hair, and/or clothing. The technology uses part-based models, e.g., Poselets, to locate multiple normalized part patches from an image. The normalized part patches are provided into trained convolutional neural networks to generate feature data. Each convolution neural network applies multiple stages of convolution operations to one part patch to generate a set of fully connected feature data. The feature data for all part patches are concatenated and then provided into multiple trained classifiers (e.g., linear support vector machines) to predict attributes of the image.
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