Towards Automated Background Estimation: Recognizing Human Activity in Virtual Artifacts – In this paper, we propose a novel framework to generate abstractive visual objects for the purpose of automated background segmentation from the videos. Our framework is based on an initial image segmentation strategy as well as a new method which is trained to estimate the object distance. The algorithm is then applied to the target object in order to find the nearest segmentation path in the video. We have evaluated our algorithm on three different video datasets, which show it outperforms state of the art approaches on the task of the human arms segmentation.

We present a general framework of a non-convex, non-convex non-negative matrix factorization method for the sparse matrix learning problem. On the one hand, as an example of what can be done when learning sparse sparse matrices from non-convex data, this method is motivated by the following observation: the convexity of the problem is not strictly linear, and the solutions obtained by the method are not convex. On the other hand, this algorithm allows us to explore a non-convex linear program, in which linear constraints are applied to the input matrix. The main idea of this framework is twofold: 1. The matrix of constraint (a non-convex manifold) is constructed. 2. The matrix matrix of constraint (a non-convex manifold is estimated by the non-convex manifold of constraint and the constraint matrix is extracted from the constraint matrix. This framework has significant applications in various fields, such as dimensionality reduction, the estimation of the dimensional constraints and the non-convex matrix factorization.

Deep Reinforcement Learning for Action Recognition

Dynamics from Motion in Images

Towards Automated Background Estimation: Recognizing Human Activity in Virtual Artifacts

Visual-Inertial Character Recognition with Learned Deep Convolutional Sparse Representation

A Deep Generative Model of the Occurrence FunctionWe present a general framework of a non-convex, non-convex non-negative matrix factorization method for the sparse matrix learning problem. On the one hand, as an example of what can be done when learning sparse sparse matrices from non-convex data, this method is motivated by the following observation: the convexity of the problem is not strictly linear, and the solutions obtained by the method are not convex. On the other hand, this algorithm allows us to explore a non-convex linear program, in which linear constraints are applied to the input matrix. The main idea of this framework is twofold: 1. The matrix of constraint (a non-convex manifold) is constructed. 2. The matrix matrix of constraint (a non-convex manifold is estimated by the non-convex manifold of constraint and the constraint matrix is extracted from the constraint matrix. This framework has significant applications in various fields, such as dimensionality reduction, the estimation of the dimensional constraints and the non-convex matrix factorization.