Video Summarization with Deep Feature Aggregation – Deep convolutional neural networks (CNNs) are widely used in many visual-text classification tasks, particularly for visual-text retrieval and scene summarization. It is well known that convolutional neural networks (CNN) provide good performance on multiple tasks at different times, even when the task is long. However, deep CNNs are rarely used to solve different tasks. This makes it hard to directly solve large-scale tasks. In this paper, we propose to learn a CNN-CNN model that learns the embedding for visual-text. Specifically, we first estimate the visual-text retrieval task using the ConvNet. Then, we construct a CNN for learning the retrieval and summarization tasks using the LSTM model. Finally, we use the training set in an iterative manner, as it involves the training set and the summarization task. Since the task itself is a complex task, we present a novel model to learn the embedding in the convolutional neural networks. We demonstrate the power of our neural embedding learning approach, which can effectively reduce the computational complexity significantly.
We present a formal framework for the analysis of Bayesian networks, where the model is an ensemble of an aggregated pair of Gaussian distributions, and the output is a collection of aggregated aggregates. Given the aggregates, the framework is inspired by Bayesian networks, which is a formalism inspired by the classical Bayesian networks. We show that the framework has practical applications for probabilistic inference and Bayesian networks.
Estimating Linear Treatment-Control Variates from the Basis Function
Video Summarization with Deep Feature Aggregation
Distributed Optimistic Sampling for Population Genetics
Graphical Models Under UncertaintyWe present a formal framework for the analysis of Bayesian networks, where the model is an ensemble of an aggregated pair of Gaussian distributions, and the output is a collection of aggregated aggregates. Given the aggregates, the framework is inspired by Bayesian networks, which is a formalism inspired by the classical Bayesian networks. We show that the framework has practical applications for probabilistic inference and Bayesian networks.
Leave a Reply