Deep Semantic Ranking over the Manifold of Pedestrians for Unsupervised Image Segmentation


Deep Semantic Ranking over the Manifold of Pedestrians for Unsupervised Image Segmentation – We present a new approach to extracting semantic representation of images in a Bayesian network with a large number of images. This approach, termed as a cross-covariant network (ICNN), is a fast and flexible method for image segmentation that has been compared to previous approaches. A thorough evaluation of our ICNN method on several benchmark datasets shows that our ICNN outperforms the previous ones by a significant margin and is a good candidate for future large scale applications.

The main objective of this paper is to build a new framework for efficient and scalable prediction. First, a set of algorithms is trained jointly with the stochastic gradient method. Then, a stochastic gradient algorithm is proposed based on a deterministic variational model with a Bayes family of random variables. The posterior distribution of the stochastic gradient is used for inference and the random variable is estimated using a polynomial-time Monte Carlo approach. The proposed method is demonstrated with the MNIST, MNIST-2K and CIFAR-10 data sets.

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Deep Semantic Ranking over the Manifold of Pedestrians for Unsupervised Image Segmentation

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  • Towards A Foundation of Comprehensive Intelligent Agents for Smart Cities

    Robust Decomposition Based on Robust Compressive BoundsThe main objective of this paper is to build a new framework for efficient and scalable prediction. First, a set of algorithms is trained jointly with the stochastic gradient method. Then, a stochastic gradient algorithm is proposed based on a deterministic variational model with a Bayes family of random variables. The posterior distribution of the stochastic gradient is used for inference and the random variable is estimated using a polynomial-time Monte Carlo approach. The proposed method is demonstrated with the MNIST, MNIST-2K and CIFAR-10 data sets.


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