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The word embedding models are capable of capturing the semantic content of the textual words. The process of extracting a set of word embedding vectors from a text document is similar to the feature extraction step of the Bag-of-Features pipeline, which is usually used in computer vision tasks. That gives rise to the Bag-of-Embedded Words (BoEW) model. In this paper a novel learning technique that...
One of the most important cues for human communication is the interpretation of facial expressions. We present a novel computer vision approach for Action Unit (AU) recognition based upon a deep learning framework combined with a semantic context model. We introduce a new convolutional neural network training loss specific to AU intensity that utilizes a binned cross entropy method to fine-tune an...
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