Towards Unified Deep Learning Model for NSFW Image and Video Captioning

AbstractRobot vision provides the most important information to robots so that they can read the context and interact with human partners successfully. Moreover, to allow humans recognize the robot’s visual understanding during human-robot interaction (HRI), the best way is for the robot to provide an explanation of its understanding in natural language. In this paper, we propose a new approach by which to interpret robot vision from an egocentric standpoint and generate descriptions to explain egocentric videos particularly for HRI. Because robot vision equals to egocentric video on the robot’s side, it contains as much egocentric view information as exocentric view information. Thus, we propose a new dataset, referred to as the global, action, and interaction (GAI) dataset, which consists of egocentric video clips and GAI descriptions in natural language to represent both egocentric and exocentric information. The encoder-decoder based deep learning model is trained based on the GAI dataset and its performance on description generation assessments is evaluated. We also conduct experiments in actual environments to verify whether the GAI dataset and the trained deep learning model can improve a robot vision system

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Deep Learning Model Selection of Suboptimal Complexity

Автоматика и телемеханика ◽

10.31857/s000523100001252-1 ◽

2018 ◽

pp. 129-147

Author(s):

Oleg Bakhteev ◽

◽

Vadim Strijov ◽

Keyword(s):

Deep Learning ◽

Model Selection ◽

Learning Model ◽

Deep Learning Model ◽

Selection Of

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Improving Sentiment Analysis using Hybrid Deep Learning Model

Recent Advances in Computer Science and Communications ◽

10.2174/2213275912666190328200012 ◽

2020 ◽

Vol 13 (4) ◽

pp. 627-640 ◽

Cited By ~ 1

Author(s):

Avinash Chandra Pandey ◽

Dharmveer Singh Rajpoot

Keyword(s):

Neural Network ◽

Deep Learning ◽

Sentiment Analysis ◽

Classification Accuracy ◽

Short Term Memory ◽

Computational Cost ◽

Extraction Process ◽

Learning Model ◽

Sentiment Classification ◽

Deep Learning Model

Background: Sentiment analysis is a contextual mining of text which determines viewpoint of users with respect to some sentimental topics commonly present at social networking websites. Twitter is one of the social sites where people express their opinion about any topic in the form of tweets. These tweets can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment analysis methods use manually extracted features for opinion classification. The manual feature extraction process is a complicated task since it requires predefined sentiment lexicons. On the other hand, deep learning methods automatically extract relevant features from data hence; they provide better performance and richer representation competency than the traditional methods. Objective: The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the computational cost. Method: To achieve the objective, a hybrid deep learning model, based on convolution neural network and bi-directional long-short term memory neural network has been introduced. Results: The proposed sentiment classification method achieves the highest accuracy for the most of the datasets. Further, from the statistical analysis efficacy of the proposed method has been validated. Conclusion: Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover, performance can also be enhanced by tuning the hyper parameters of deep leaning models.

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