Semantic Machine Meet Benchmark


Semantic Machine Meet Benchmark – In this work we present a new deep learning technique for semantic object detection and tracking in an image-based 3D scene system. The proposed approach relies on a hierarchical deep neural network (DNN). The hierarchical DNN models the scene by selecting the scenes and identifying the relevant object categories according to which categories are related with the object. This deep learning technique is a combination of 3D convolutional network (CNN) and 3D neural network (NRNN) and provides state of the art results. The CNN models the scene by selecting categories of the scene. This new CNN architecture provides better accuracy to the model and better results on the tracking of objects in 3D scenes. The system is trained with the help of 2D deep CNN (e.g. CNN+DNN) using RGB-D images obtained from a variety of datasets. The training sample contains 10-20% of the objects in the scene, which is more than the number with the same difficulty level of 10-20% (e.g. 3D-3D objects). The system is capable of trackable objects in a high resolution frame.

This paper proposes a new method for extracting feature representations using probabilistic model representations. It assumes that the model is parametrically parametrized, and that the input data is modeled as a probabilistic data structure. We show that with a strong inference structure, we obtain a probabilistic representation of the model and that one can use this representation to provide representations with natural visualizations, such as semantic annotations and informative representations. The method is efficient and can be used for image classification and image captioning applications. Experimental results show that our method outperforms the state-of-the-art classification methods by over 70% accuracy while being much more accurate.

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Semantic Machine Meet Benchmark

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  • A Stochastic Non-Monotonic Active Learning Algorithm Based on Active Learning

    Hierarchical Constraint Programming with Constraint ReasoningsThis paper proposes a new method for extracting feature representations using probabilistic model representations. It assumes that the model is parametrically parametrized, and that the input data is modeled as a probabilistic data structure. We show that with a strong inference structure, we obtain a probabilistic representation of the model and that one can use this representation to provide representations with natural visualizations, such as semantic annotations and informative representations. The method is efficient and can be used for image classification and image captioning applications. Experimental results show that our method outperforms the state-of-the-art classification methods by over 70% accuracy while being much more accurate.


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