Video Anomaly Detection Using Learned Convnet Features


Video Anomaly Detection Using Learned Convnet Features – This paper addresses the problem of learning a discriminative image of a person from two labeled images. Existing approaches address this problem by using latent representation learning and latent embedding. However, the underlying latent embedding structure often fails to capture the underlying person identity structure. In this paper, proposed approaches address this problem by learning deep representations of latent spaces. These representations are learned using the image features that have been captured from a shared space, thus providing a more robust discriminative model of the person. Extensive numerical experiments on two publicly available datasets demonstrate the effectiveness of our proposed approach. The results indicate that our approach can be used for person identification tasks in a non-convex problem with high dimensionality.

We analyze the problem of text-to-translation (TTS) and its algorithms in two contexts: translation evaluation and annotation. We propose an efficient and flexible method for the latter. Our approach utilizes large collection of annotating texts using high level knowledge of their syntactical structure. We propose a method of combining this information to form an evaluation for three-level classification (i.e. category, word level) of a TTS. The evaluation requires two steps: a sequence-to-sequence algorithm that optimizes the data and a method that computes a new classification goal. We evaluate our approach using a task of the application of speech recognition to texts of Arabic. Our framework provides a new approach to transcribing text, leveraging a large collection of annotations and knowledge of the syntactical structures of Arabic. It also is applied to the classification of text in two different scenarios: annotation based or text-to-translation.

Convex Sparsification of Unstructured Aggregated Data

Learning Sparse Representations of Data with Regularized Dropout

Video Anomaly Detection Using Learned Convnet Features

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  • Poseidon: An Efficient Convolutional Neural Network for Automatic Detection of Severe Sleep Apnea

    A novel approach to text-to-translationWe analyze the problem of text-to-translation (TTS) and its algorithms in two contexts: translation evaluation and annotation. We propose an efficient and flexible method for the latter. Our approach utilizes large collection of annotating texts using high level knowledge of their syntactical structure. We propose a method of combining this information to form an evaluation for three-level classification (i.e. category, word level) of a TTS. The evaluation requires two steps: a sequence-to-sequence algorithm that optimizes the data and a method that computes a new classification goal. We evaluate our approach using a task of the application of speech recognition to texts of Arabic. Our framework provides a new approach to transcribing text, leveraging a large collection of annotations and knowledge of the syntactical structures of Arabic. It also is applied to the classification of text in two different scenarios: annotation based or text-to-translation.


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