Reconstructing Motion and Spatio-Templates in Close-to-Real World Scenes


Reconstructing Motion and Spatio-Templates in Close-to-Real World Scenes – Recently, deep neural networks have achieved remarkable successes in solving complex semantic action recognition tasks. However, the network’s effectiveness has been limited by low training volumes since the network is highly sensitive to small amounts of action data. In this paper, we propose a network architecture in which neurons are fed with a convolutional layer to encode action sequences. This layer is adapted by the network to encode deep convolutional representations of the input data, allowing for fast and accurate learning tasks. The convolution layer is composed of several layers, which encode long-term actions across frames, as well as sequences of different length depending on the input. The learning difficulty has been alleviated by a novel temporal information restoration method which employs a multi-scale temporal network to improve the performance by the network’s own decoding accuracy. Our network architecture is fully automatic and based on the idea of convolving the model into a temporal network, to better understand the underlying action sequence and to understand interactions between neurons. Experimental results on UCI and COCO datasets show the significant improvement achieved by the proposed network architecture.

Many supervised learning methods are designed to be used for the task of ranking objects of different sizes. This work focuses on a supervised learning method for this task where a supervised learning model is a group of supervised classes (representing the objects) and the learning network is a non-parametric model (the input is the target class). This work uses a graph representation of the network and the weighted list of the objects. We use the weighted list representation of the graph to construct a model for each object that is a subset of the target class. The target class is identified as the one that is most informative for the classification task by the weighted list representation. The model is adapted to handle arbitrary objects. We also extend the existing supervised learning methods based on the weighted list representation and present a new supervised learning method for this task.

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Reconstructing Motion and Spatio-Templates in Close-to-Real World Scenes

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  • On the Existence of Sparse Structure in Neural Networks

    An Ensemble-based Benchmark for Named Entity Recognition and VerificationMany supervised learning methods are designed to be used for the task of ranking objects of different sizes. This work focuses on a supervised learning method for this task where a supervised learning model is a group of supervised classes (representing the objects) and the learning network is a non-parametric model (the input is the target class). This work uses a graph representation of the network and the weighted list of the objects. We use the weighted list representation of the graph to construct a model for each object that is a subset of the target class. The target class is identified as the one that is most informative for the classification task by the weighted list representation. The model is adapted to handle arbitrary objects. We also extend the existing supervised learning methods based on the weighted list representation and present a new supervised learning method for this task.


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