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 propose to efficiently train recurrent multi-task learning models by using random walk (RK), and show that these models are more reliable and perform more efficiently than the state of the art. We apply our RK model to the learning of large-scale multilayer perceptrons, show that it outperforms other state of the art models.

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Video Anomaly Detection Using Learned Convnet Features

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  • The Power of Multiscale Representation for Accurate 3D Hand Pose Estimation

    Fast Task Selection via Recurrent Residual NetworksWe propose to efficiently train recurrent multi-task learning models by using random walk (RK), and show that these models are more reliable and perform more efficiently than the state of the art. We apply our RK model to the learning of large-scale multilayer perceptrons, show that it outperforms other state of the art models.


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