Learning to detect individuals with multiple pre-images in long-term infrared images using adaptive feature selection


Learning to detect individuals with multiple pre-images in long-term infrared images using adaptive feature selection – Recent years have witnessed the growth of social applications, such as video chat, which have proven to be challenging to solve. In this paper, we propose a novel method for facial recognition in videos. Specifically, we train a Deep Convolutional Neural Network (DCNN) to generate and annotate short snippets of the video frames. For these samples, we select an eye-level annotation of the frames and evaluate the performance by means of a series of experiments on different datasets. For training of the DCNN, we train it by using two different algorithms: one trained by hand and the other by using CNNs. We show that we obtain competitive and improved performance on both datasets: we achieve a performance over 95% accuracy.

We present a deep learning approach for modeling a real-valued function in the form of a Bayesian network. We propose a novel hierarchical Bayesian network (HBN) for this task and show that it can be used efficiently. We first build deep architectures for neural networks and then propose a new hierarchical Bayesian network (HBN) based on a novel model of the function. We show that it can be used efficiently in both supervised and unsupervised settings, demonstrating that this approach can be used for the task at hand.

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Learning to detect individuals with multiple pre-images in long-term infrared images using adaptive feature selection

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  • A Comprehensive Evaluation of BDA in Multilayer Human Dataset

    Snorkel: Efficient Strict Relaxations for Deep Neural NetworksWe present a deep learning approach for modeling a real-valued function in the form of a Bayesian network. We propose a novel hierarchical Bayesian network (HBN) for this task and show that it can be used efficiently. We first build deep architectures for neural networks and then propose a new hierarchical Bayesian network (HBN) based on a novel model of the function. We show that it can be used efficiently in both supervised and unsupervised settings, demonstrating that this approach can be used for the task at hand.


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