Interpretable Deep Text and Image Matching with LSTM


Interpretable Deep Text and Image Matching with LSTM – LSTM has been successfully used to model human visual attention in a variety of applications. However, existing approaches are not optimized for complex visual attention scenarios where the visual attention is typically directed towards a visual object in a visual domain; they need to model both the temporal location (e.g., human body in a pose) as well as the feature representation extracted from the data. We propose a novel deep model, which simultaneously produces object recognition results and object category recognition results for each pose space. This makes the object category recognition framework scalable to large datasets, where it is useful for handling large, complex scenarios with large and complex human representations. We evaluate several proposed deep architectures and discuss how different methods can be effectively applied to our system.

In this paper, we first propose a novel deep convolutional neural network (CNN) architecture to automatically adaptively learn classification models. We then show that the architecture can be used to improve the class performance of a CNN model. We show that our CNN architecture achieves the best overall classification performance.

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Interpretable Deep Text and Image Matching with LSTM

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  • Generating High-Quality Face Images from Video with a Multi-Modal Deep Learning Framework

    Crowdsourcing the Classification Imputation with Sparsity RegularizationIn this paper, we first propose a novel deep convolutional neural network (CNN) architecture to automatically adaptively learn classification models. We then show that the architecture can be used to improve the class performance of a CNN model. We show that our CNN architecture achieves the best overall classification performance.


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