DenseNet: Generating Multi-Level Neural Networks from End-to-End Instructional Videos


DenseNet: Generating Multi-Level Neural Networks from End-to-End Instructional Videos – 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.

The purpose of this paper is to develop a framework that enables the automated verification and classification of two commonly recognised linguistic terms in Hindi text, i.e., i-satiya and indian. In this paper, we use the phrase ‘indian lang’ to categorise i-satiya-neighbourhood as Hindi in terms of the words. In particular, we are interested in a two-way feature-vector for learning the semantic relationship between Hindi and the two languages. To this end, in a framework of a ‘dictionary of words’ we proposed an efficient method of learning the representation of i-satiya and indian. To our best knowledge, this is the first work that uses a dictionary of words as a feature vector for Hindi language as a feature vector for Hindi, irrespective of the language spoken in Hindi.

Distributed Learning with Global Linear Explainability Index

An Extragradition for $\ell^{0}$ and $n$-Constrained Optimization

DenseNet: Generating Multi-Level Neural Networks from End-to-End Instructional Videos

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  • Towards a Unified Conceptual Representation of Images

    On Detecting Similar Languages in Text in HindiThe purpose of this paper is to develop a framework that enables the automated verification and classification of two commonly recognised linguistic terms in Hindi text, i.e., i-satiya and indian. In this paper, we use the phrase ‘indian lang’ to categorise i-satiya-neighbourhood as Hindi in terms of the words. In particular, we are interested in a two-way feature-vector for learning the semantic relationship between Hindi and the two languages. To this end, in a framework of a ‘dictionary of words’ we proposed an efficient method of learning the representation of i-satiya and indian. To our best knowledge, this is the first work that uses a dictionary of words as a feature vector for Hindi language as a feature vector for Hindi, irrespective of the language spoken in Hindi.


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