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Classical dictionary learning algorithms that rely on a single source of information have been successfully used for the discriminative tasks. However, exploiting multiple sources has demonstrated its effectiveness in solving challenging real-world situations. We propose a new framework for feature fusion to achieve better classification performance as compared to the case where individual sources...
In this paper, we propose a new approach for the person re-identification problem, discovering the correct matches for a query pedestrian image from a set of gallery images. It is well motivated by our observation that the overall complex inter-camera transformation, caused by the change of camera viewpoints, person poses and view illuminations, can be effectively modelled by a combination of many...
This paper proposes a new scheme for the 2D-3D face recognition problem. Our proposed framework mainly consists of Restricted Boltzmann Machines (RBMs) and a correlation learning model. In the framework, a single-layer network based on RBMs is adopted to extract latent features over two different modalities. Furthermore, the latent hidden layer features of different models are projected to formulate...
Human age provides key demographic information. It is also considered as an important soft biometric trait for human identification or search. Compared to other pattern recognition problems (e.g., object classification, scene categorization), age estimation is much more challenging since the difference between facial images with age variations can be more subtle and the process of aging varies greatly...
Restricted Boltzmann Machine (RBM) has been successfully applied to unsupervised learning and intensity modeling of images. In this paper, we cast background subtraction as an image recovery and foreground residual estimation problem within the RBM hierarchy. We propose a partially-sparse RBM (PS-RBM) framework which models the image as the integration of the trained RBM weights where the weights...
In MAS, model-free action-value based reinforcement learning, such as Q-learning, suffers from the fact that both the state and the action space scale exponentially with the number of agents, the learning process is very slow and low efficiency, meanwhile, the convergence of multi-agent reinforcement learning is not guaranteed when ideal assumptions do not hold. To solve the question, this paper proposes...
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