Learning a graph language model for action recognition


Learning a graph language model for action recognition – Deep learning has shown a lot of promise in many areas. As a first step, deep learning is used to solve the task of classifying an input vector into a category of classifiers. This has been one of the biggest challenges in the recent years. In this work, a deep network architecture is proposed for action recognition under the task of learning visual feature representations. It achieves state of the art performance, although a challenging task. We will show that the proposed architecture can also be used for both classification and retrieval tasks.

We propose a generic approximation method to improve the precision of the posterior distributions. Our method assumes the posterior is a finite sequence of arbitrary-valued non-Gaussian variables. We use a logistic regression model to evaluate the posterior distribution and prove a negative belief matrix. We also define a general relaxation of the bounds, which guarantees the method’s convergence.

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Learning a graph language model for action recognition

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  • Extense-aware Word Sense Disambiguation by Sparse Encoding of Word Descriptors

    Optimal Convex Margin Estimation with Arbitrary Convex PriorsWe propose a generic approximation method to improve the precision of the posterior distributions. Our method assumes the posterior is a finite sequence of arbitrary-valued non-Gaussian variables. We use a logistic regression model to evaluate the posterior distribution and prove a negative belief matrix. We also define a general relaxation of the bounds, which guarantees the method’s convergence.


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