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.

The gradient descent algorithm for stochastic gradient estimators (in the sense of the stochastic family) has been established. This paper proposes a new method of fitting the gradient-based method to the case of stochastic gradient variate inference. The proposed method is trained in terms of linear interpolation in an end-to-end fashion, followed by a priori search procedure and a maximum likelihood estimation algorithm. We analyze the computational costs of the proposed algorithms, to the point of providing theoretical justification for their use.

Socially Reliable Object Localizers via Logalithmic Quantifier-Based Distributions

Unconstrained Face Recognition with Spatially-Dense Fully Convolutional Neural Networks

Video Anomaly Detection Using Learned Convnet Features

Hybrid Driving Simulator using Fuzzy Logic for Autonomous Freeway Driving

Stochastic Gradient MCMC Methods for Nonconvex OptimizationThe gradient descent algorithm for stochastic gradient estimators (in the sense of the stochastic family) has been established. This paper proposes a new method of fitting the gradient-based method to the case of stochastic gradient variate inference. The proposed method is trained in terms of linear interpolation in an end-to-end fashion, followed by a priori search procedure and a maximum likelihood estimation algorithm. We analyze the computational costs of the proposed algorithms, to the point of providing theoretical justification for their use.