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This paper presents a new learning approach for single-frame face super-resolution (SR). The aim of face SR is to estimate the missing high-resolution (HR) information from a single low-resolution (LR) face image by learning from training samples in the database. A commonly encountered issue in conventional face SR methods is that when the given LR image is a new face significantly different from...
In this paper, we propose an enhanced approach to improving our previous method which employs syntactic structures (verb-object pairs) to identify latent user goals. Our new approach employs a supervised-learning method to learn hint verbs and considers URL information and title information to classify snippets into three coarse categories, which are resource-seeking, informational, and navigational...
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