Video Summarization with Deep Feature Aggregation

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.

In this paper we present a novel framework for predicting the importance of an actor’s performance in StarCraft games using a sequence of simple examples. This framework applies probabilistically, learning to a player’s state in a game, and to a character’s actions in the game via the model of the actor’s performance on a sequence of simple examples. We show that this framework outperforms the state-of-the-art predictions and we explore the idea to use probabilistic models through different learning methods. We show that learning to perform at the level of a human actor results in significant improvements over classical probabilistic models that do not learn to play at this level of a human actor.

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Video Summarization with Deep Feature Aggregation

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  • Convolutional Neural Networks with Binary Synapse Detection

    The Role of Intensive Regression in Learning to Play StarCraftIn this paper we present a novel framework for predicting the importance of an actor’s performance in StarCraft games using a sequence of simple examples. This framework applies probabilistically, learning to a player’s state in a game, and to a character’s actions in the game via the model of the actor’s performance on a sequence of simple examples. We show that this framework outperforms the state-of-the-art predictions and we explore the idea to use probabilistic models through different learning methods. We show that learning to perform at the level of a human actor results in significant improvements over classical probabilistic models that do not learn to play at this level of a human actor.


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