Eliminating Dither in RGB-based 3D Face Recognition with Deep Learning: A Unified Approach


Eliminating Dither in RGB-based 3D Face Recognition with Deep Learning: A Unified Approach – Many computer vision tasks involve segmentation and analysis, both important aspects of the task at hand. We present a novel approach to automatic segmentation of facial features from face images. Our method is simple and fast, and works well when trained in supervised (i.e., on the face image from the training set) or unlabeled (i.e., on the face images from the unlabeled set). To learn a discriminative model for a particular task, we first train a discriminative model for each face image in order to extract a global discriminative representation from the face images. Our system is evaluated on a set of datasets from a large-scale multi-view face recognition system. The results indicate that the discriminative model learned by our method consistently outperforms the unlabeled models with respect to a variety of segmentation and analysis tasks. Our system is able to recognize faces with low or no annotation cost.

We present an unsupervised learning approach for visual search that learns to extract relevant information from unstructured texts. These books are often classified according to their content, but often contain very few annotations. This has been an issue with previous unsupervised methods, which use a dictionary function to extract relevant words from the data. In this paper, we propose a new unsupervised learning approach that extracts useful information from unstructured text. We first learn a model that directly predicts which words in a text have content and then use a dictionary to infer the word’s content without the annotation. The method can extract the most relevant words for each word in a text by simply applying a pre-processing step. Our method is simple to implement, flexible enough to handle large and complex datasets, and is applicable to any unsupervised visual dataset without needing the annotation process. In the experiment on the Amazon Alexa dataset, our method achieved comparable or better accuracy than our supervised learning solution.

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Eliminating Dither in RGB-based 3D Face Recognition with Deep Learning: A Unified Approach

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  • Multi-View Conditional Gradient Approach to Action Recognition

    Crowdsourcing Classification of Unstructured Anomalies in Financial DataWe present an unsupervised learning approach for visual search that learns to extract relevant information from unstructured texts. These books are often classified according to their content, but often contain very few annotations. This has been an issue with previous unsupervised methods, which use a dictionary function to extract relevant words from the data. In this paper, we propose a new unsupervised learning approach that extracts useful information from unstructured text. We first learn a model that directly predicts which words in a text have content and then use a dictionary to infer the word’s content without the annotation. The method can extract the most relevant words for each word in a text by simply applying a pre-processing step. Our method is simple to implement, flexible enough to handle large and complex datasets, and is applicable to any unsupervised visual dataset without needing the annotation process. In the experiment on the Amazon Alexa dataset, our method achieved comparable or better accuracy than our supervised learning solution.


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