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.

Objects are often used in various scientific disciplines and have been used for various applications. In this paper, we propose a new approach to object detection from visual data. We propose a novel visual-based object detection method, which is based on a hierarchical convolutional neural network, which achieves a similar performance as the state-of-the-art object detection. However, we also propose a simple and efficient method which has a fast convergence rate, which is much faster than current state-of-the-art object detection methods. We demonstrate the proposed model for using visual data in the learning of spatial semantic concepts, which is the main reason why it is capable to solve various tasks in object detection such as classification and recognition.

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Video Summarization with Deep Feature Aggregation

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  • Deep Sparsity: A Distributed Representation of Deep Neural Networks

    A New Spectral Feature Selection Method for Object Detection in Unstructured ContextsObjects are often used in various scientific disciplines and have been used for various applications. In this paper, we propose a new approach to object detection from visual data. We propose a novel visual-based object detection method, which is based on a hierarchical convolutional neural network, which achieves a similar performance as the state-of-the-art object detection. However, we also propose a simple and efficient method which has a fast convergence rate, which is much faster than current state-of-the-art object detection methods. We demonstrate the proposed model for using visual data in the learning of spatial semantic concepts, which is the main reason why it is capable to solve various tasks in object detection such as classification and recognition.


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