Semi-automatic Data Annotation Tool for Person Re-identification Across Multi Cameras

2018 ◽  
Author(s):  
Tianyi Zhao ◽  
Shengcai Liao ◽  
Zhen Lei
Keyword(s):  
Annotation Tool ◽  
Automatic Data ◽  
Data Annotation

scholarly journals DeepTrigger: A Watermarking Scheme of Deep Learning Models Based on Chaotic Automatic Data Annotation

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 213296-213305
Author(s):  
Ying-Qian Zhang ◽  
Yi-Ran Jia ◽  
Xingyuan Wang ◽  
Qiong Niu ◽  
Nian-Dong Chen
Keyword(s):  
Deep Learning ◽  
Learning Models ◽  
Automatic Data ◽  
Data Annotation ◽  
Watermarking Scheme

scholarly journals Semi-Automatic Data Annotation, POS Tagging and Mildly Context-Sensitive Disambiguation: The eXtended Revised AraMorph (XRAM)

2018 ◽  
pp. 155-168
Author(s):  
Giuliano Lancioni ◽  
Laura Garofalo ◽  
Raoul Villano ◽  
Francesca Romana Romani ◽  
Marta Campanelli ◽  
...  
Keyword(s):  
Automatic Data ◽  
Data Annotation ◽  
Context Sensitive ◽  
Pos Tagging

scholarly journals Semi-Automatic Data Annotation guided by Feature Space Projection

2020 ◽  
Author(s):  
Bárbara C. Benato ◽  
Alexandru C. Telea ◽  
Alexandre X. Falcão
Keyword(s):  
Visual Analytics ◽  
Visual Analysis ◽  
Intestinal Parasites ◽  
Feature Space ◽  
Training Sample ◽  
Added Value ◽  
Automatic Data ◽  
Data Annotation ◽  
Unseen Data ◽  
Space Projection

Data annotation using visual inspection (supervision) of each training sample can be laborious. Interactive solutions alleviate this by helping experts propagate labels from a few supervised samples to unlabeled ones based solely on the visual analysis of their feature space projection (with no further sample supervision). We present a semi-automatic data annotation approach based on suitable feature space projection and semi-supervised label estimation. We validate our method on the popular MNIST dataset and on images of human intestinal parasites with and without fecal impurities, a large and diverse dataset that makes classification very hard. We evaluate two approaches for semi-supervised learning from the latent and projection spaces, to choose the one that best reduces user annotation effort and also increases classification accuracy on unseen data. Our results demonstrate the added-value of visual analytics tools that combine complementary abilities of humans and machines for more effective machine learning.

scholarly journals Semi-automatic data annotation guided by feature space projection

2021 ◽  
Vol 109 ◽  
pp. 107612
Author(s):  
Bárbara C. Benato ◽  
Jancarlo F. Gomes ◽  
Alexandru C. Telea ◽  
Alexandre X. Falcão
Keyword(s):  
Feature Space ◽  
Automatic Data ◽  
Data Annotation ◽  
Space Projection

scholarly journals Cellar: Interactive single cell data annotation tool

2021 ◽  
Author(s):  
Euxhen Hasanaj ◽  
Jingtao Wang ◽  
Arjun Sarathi ◽  
Jun Ding ◽  
Ziv Bar-Joseph
Keyword(s):  
Single Cell ◽  
Software Tool ◽  
Cell Types ◽  
Annotation Tool ◽  
Interactive Software ◽  
Cell Level ◽  
Data Annotation ◽  
Single Cell Sequencing ◽  
Molecular Signals ◽  
Cell Data

AbstractSeveral recent technologies and platforms enable the profiling of various molecular signals at the single-cell level. A key question for all studies using such data is the assignment of cell types. To improve the ability to correctly assign cell types in single and multi-omics sequencing and imaging single-cell studies, we developed Cellar. This interactive software tool supports all steps in the analysis and assignment process. We demonstrate the advantages of Cellar by using it to annotate several HuBMAP datasets from multi-omics single-cell sequencing and spatial proteomics studies. Cellar is freely available and includes several annotated reference HuBMAP datasets.Availabilityhttps://data.test.hubmapconsortium.org/app/cellar

Proximity based automatic data annotation for autonomous driving

2020 ◽  
Vol 7 (2) ◽  
pp. 395-404 ◽  
Author(s):  
Chen Sun ◽  
Jean M. Uwabeza Vianney ◽  
Ying Li ◽  
Long Chen ◽  
Li Li ◽  
...  
Keyword(s):  
Autonomous Driving ◽  
Automatic Data ◽  
Data Annotation
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