User Input Based Style Transfer While Retaining Facial Attributes

2019 ◽  
Author(s):  
Sharan Pai ◽  
Nikhil Sachdeva ◽  
Rajiv Shah ◽  
Roger Zimmermann
Keyword(s):  
Style Transfer ◽  
User Input ◽  
Facial Attributes

A Model for Structured Data Entry Based on Explicit Descriptional Knowledge

1994 ◽  
Vol 33 (05) ◽  
pp. 454-463 ◽  
Author(s):  
A. M. van Ginneken ◽  
J. van der Lei ◽  
J. H. van Bemmel ◽  
P. W. Moorman
Keyword(s):  
Domain Knowledge ◽  
Data Entry ◽  
Knowledge Bases ◽  
Structured Data ◽  
Research Quality ◽  
Free Text ◽  
Specific Knowledge ◽  
User Input ◽  
The One ◽  
Research Quality Assessment

Abstract:Clinical narratives in patient records are usually recorded in free text, limiting the use of this information for research, quality assessment, and decision support. This study focuses on the capture of clinical narratives in a structured format by supporting physicians with structured data entry (SDE). We analyzed and made explicit which requirements SDE should meet to be acceptable for the physician on the one hand, and generate unambiguous patient data on the other. Starting from these requirements, we found that in order to support SDE, the knowledge on which it is based needs to be made explicit: we refer to this knowledge as descriptional knowledge. We articulate the nature of this knowledge, and propose a model in which it can be formally represented. The model allows the construction of specific knowledge bases, each representing the knowledge needed to support SDE within a circumscribed domain. Data entry is made possible through a general entry program, of which the behavior is determined by a combination of user input and the content of the applicable domain knowledge base. We clarify how descriptional knowledge is represented, modeled, and used for data entry to achieve SDE, which meets the proposed requirements.

Feature Fusion from Multiple Paintings for Generalized Artistic Style Transfer

2019 ◽  
Author(s):  
Utsav Krishnan ◽  
Akshal Sharma ◽  
Pratik Chattopadhyay
Keyword(s):  
Feature Fusion ◽  
Style Transfer ◽  
Artistic Style

LIRKIS Global Collaborative Virtual Environments: Current State and Utilization Perspective

2020 ◽  
Vol 11 (1) ◽  
pp. 99-106
Author(s):  
Marián Hudák ◽  
Štefan Korečko ◽  
Branislav Sobota
Keyword(s):  
Virtual Reality ◽  
Virtual Environments ◽  
Smart Devices ◽  
Collaborative Virtual Environments ◽  
Web Based ◽  
Smart Client ◽  
Input Devices ◽  
User Input ◽  
History Of ◽  
Shared Virtual Environments

AbstractRecent advances in the field of web technologies, including the increasing support of virtual reality hardware, have allowed for shared virtual environments, reachable by just entering a URL in a browser. One contemporary solution that provides such a shared virtual reality is LIRKIS Global Collaborative Virtual Environments (LIRKIS G-CVE). It is a web-based software system, built on top of the A-Frame and Networked-Aframe frameworks. This paper describes LIRKIS G-CVE and introduces its two original components. The first one is the Smart-Client Interface, which turns smart devices, such as smartphones and tablets, into input devices. The advantage of this component over the standard way of user input is demonstrated by a series of experiments. The second component is the Enhanced Client Access layer, which provides access to positions and orientations of clients that share a virtual environment. The layer also stores a history of connected clients and provides limited control over the clients. The paper also outlines an ongoing experiment aimed at an evaluation of LIRKIS G-CVE in the area of virtual prototype testing.

scholarly journals Real-time Localized Photorealistic Video Style Transfer

2021 ◽  
Author(s):  
Xide Xia ◽  
Tianfan Xue ◽  
Wei-sheng Lai ◽  
Zheng Sun ◽  
Abby Chang ◽  
...  
Keyword(s):  
Real Time ◽  
Style Transfer

scholarly journals Arbitrary Style Transfer via Multi-Adaptation Network

2020 ◽  
Author(s):  
Yingying Deng ◽  
Fan Tang ◽  
Weiming Dong ◽  
Wen Sun ◽  
Feiyue Huang ◽  
...  
Keyword(s):  
Style Transfer

Transfer learning based convolution neural net for authentication and classification of emotions from natural and stimulated speech signals

2021 ◽  
pp. 1-12
Author(s):  
Mukul Kumar ◽  
Nipun Katyal ◽  
Nersisson Ruban ◽  
Elena Lyakso ◽  
A. Mary Mekala ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Transfer Learning ◽  
Research Work ◽  
Oral Communication ◽  
Neural Net ◽  
Emotion Classification ◽  
Style Transfer ◽  
Conventional Machine ◽  
Neural Network Algorithm

Over the years the need for differentiating various emotions from oral communication plays an important role in emotion based studies. There have been different algorithms to classify the kinds of emotion. Although there is no measure of fidelity of the emotion under consideration, which is primarily due to the reason that most of the readily available datasets that are annotated are produced by actors and not generated in real-world scenarios. Therefore, the predicted emotion lacks an important aspect called authenticity, which is whether an emotion is actual or stimulated. In this research work, we have developed a transfer learning and style transfer based hybrid convolutional neural network algorithm to classify the emotion as well as the fidelity of the emotion. The model is trained on features extracted from a dataset that contains stimulated as well as actual utterances. We have compared the developed algorithm with conventional machine learning and deep learning techniques by few metrics like accuracy, Precision, Recall and F1 score. The developed model performs much better than the conventional machine learning and deep learning models. The research aims to dive deeper into human emotion and make a model that understands it like humans do with precision, recall, F1 score values of 0.994, 0.996, 0.995 for speech authenticity and 0.992, 0.989, 0.99 for speech emotion classification respectively.

Sign in / Sign up

Export Citation Format

Share Document