Lip Localization via Semi-Local Kernels


Lip Localization via Semi-Local Kernels – The paper presents a practical and robust method for learning and computing face models in the presence of natural occlusion. Our algorithm is based on a discriminative representation over faces, which is an essential step to learning the structure of a face database. We prove that both the face recognition and face estimation are NP-hard, without taking into account the presence of occlusion. We apply our method to several complex face datasets and show results on simulated and real-world datasets.

We present a novel technique for automatically inferring the joint posterior distribution of an edge map from a graph. We present a convolutional neural network (CNN) for this task, which can leverage data from discrete graphs. The CNN can easily be trained efficiently, and also learn a novel posterior representation from a graph that captures the information needed to infer the posterior. Moreover, we demonstrate that CNNs trained to infer the posterior can also be trained with state-of-the-art CNN loss models, and achieve state-of-the-art results on a variety of benchmark datasets (including a large-scale benchmark of computer vision).

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Lip Localization via Semi-Local Kernels

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    PPR-FCN with Continuous State Space Representations for Graph EmbeddingWe present a novel technique for automatically inferring the joint posterior distribution of an edge map from a graph. We present a convolutional neural network (CNN) for this task, which can leverage data from discrete graphs. The CNN can easily be trained efficiently, and also learn a novel posterior representation from a graph that captures the information needed to infer the posterior. Moreover, we demonstrate that CNNs trained to infer the posterior can also be trained with state-of-the-art CNN loss models, and achieve state-of-the-art results on a variety of benchmark datasets (including a large-scale benchmark of computer vision).


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