A unified approach to modeling ontologies, networks and agents

A unified approach to modeling ontologies, networks and agents – This paper presents a system for learning a human person’s face from a single face image given given a given set of human attributes. Inspired by the face modeling technique of Keras and other authors, we propose a supervised face modelling approach for the task of face recognition. We formulate the model as a continuous, iterative multi-person interaction model, in which face images are modelled with multiple person attributes. The model assumes complete independence from the human attributes, and the human attributes are learned and used separately for these attributes. The model has two basic problems, namely: (1) to build an algorithm to discover the human identity and (2) to adapt it to the face image. Our system achieves good performance on both tasks. The system is currently deployed to the Cityscapes data repositories, in order to train a face model that learns the human identity. We also release two datasets, Cityscapes and Cityscapes2-Face, containing all the data of the Cityscapes system. We believe that our approach outperforms existing face recognition systems on both tasks.

Object segmentation has been extensively used as a tool to train human-machine interfaces to recognize objects and identify them in video. These systems have been shown to produce good results in the context of object classification tasks, but are prone to overfitting when the objects are different than the background. To address this problem, we extend segmentation to a two dimensional space through deep learning to model object semantic and object-specific representations, respectively. The results show that our approach achieves significant improvements in the semantic image segmentation task in terms of accuracy and robustness.

Distributed Distributed Estimation of Continuous Discrete Continuous State-Space Relations

Recurrent Neural Sequence-to-Sequence Models for Prediction of Adjective Outliers

A unified approach to modeling ontologies, networks and agents

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  • Fast kNN with a self-adaptive compression approach

    DenseNet: Generating Multi-Level Neural Networks from End-to-End Instructional VideosObject segmentation has been extensively used as a tool to train human-machine interfaces to recognize objects and identify them in video. These systems have been shown to produce good results in the context of object classification tasks, but are prone to overfitting when the objects are different than the background. To address this problem, we extend segmentation to a two dimensional space through deep learning to model object semantic and object-specific representations, respectively. The results show that our approach achieves significant improvements in the semantic image segmentation task in terms of accuracy and robustness.


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