On the convergence of the divide-and-conceive algorithm for visual data fusion


On the convergence of the divide-and-conceive algorithm for visual data fusion – We present a general method for generating realistic images without human hand gestures, which is a challenging task due to the lack of accurate motion. In this work, we propose a simple and effective method to generate realistic images using gestures via an automatic image-to-image matching. The proposed method is robust to non-human object and human pose variations and can be applied to image manipulation. Experiments conducted on our dataset show that our approach has the capability of successfully generating realistic images with hand gesture representations.

We present Deep ResCoded, a new method for learning multi-level image representations. Rather than learning an image sequence from a single deep convolutional network, our method learns a set of semantic representations for each object, which in turn can be used to create more detailed representation for similar objects in the environment. Deep ResCoded achieves similar computational performances to the state-of-the art baselines on several challenging datasets.

Feature Learning for Image Search via Dynamic Contextual Policy Search

Fast and reliable indexing with dense temporal-temporal networks

On the convergence of the divide-and-conceive algorithm for visual data fusion

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  • Multilabel Classification using K-shot Digestion

    Efficient Learning of Dynamic Spatial Relations in Deep Neural Networks with Application to Object AnnotationWe present Deep ResCoded, a new method for learning multi-level image representations. Rather than learning an image sequence from a single deep convolutional network, our method learns a set of semantic representations for each object, which in turn can be used to create more detailed representation for similar objects in the environment. Deep ResCoded achieves similar computational performances to the state-of-the art baselines on several challenging datasets.


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