scholarly journals Influence of Gaze Direction on Hand Location and Orientation in a Memory-Guided Alignment Task

2019 ◽  
Vol 19 (10) ◽  
pp. 219b
Author(s):  
Gaelle N. Luabeya ◽  
Xiaogang Yan ◽  
J. D. Crawford
Keyword(s):  
Gaze Direction ◽  
Alignment Task

Mona Lisa's Eyes: No Compensation for Perceived Gaze Direction in Pictures

2004 ◽  
Author(s):  
Melanie Lunsford ◽  
Sheena Rogers ◽  
Lars Strother ◽  
Michael Kubovy
Keyword(s):  
Gaze Direction

"Eye-raceing" prejudice: The effect of gaze direction on amygdala activation in response to outgroup faces

2003 ◽  
Author(s):  
Abigail A. Baird ◽  
Jennifer A. Richeson ◽  
Heather L. Gordon ◽  
Malia F. Mason ◽  
Romero A. Hayman ◽  
...  
Keyword(s):  
Gaze Direction ◽  
Amygdala Activation

scholarly journals Transcription Alignment of Historical Vietnamese Manuscripts without Human-Annotated Learning Samples

2021 ◽  
Vol 11 (11) ◽  
pp. 4894
Author(s):  
Anna Scius-Bertrand ◽  
Michael Jungo ◽  
Beat Wolf ◽  
Andreas Fischer ◽  
Marc Bui
Keyword(s):  
Object Detection ◽  
State Of The Art ◽  
Positive Impact ◽  
Detection System ◽  
Training Data ◽  
Detection Accuracy ◽  
Current State ◽  
Alignment Task ◽  
Scanned Image ◽  
Automatic Transcription

The current state of the art for automatic transcription of historical manuscripts is typically limited by the requirement of human-annotated learning samples, which are are necessary to train specific machine learning models for specific languages and scripts. Transcription alignment is a simpler task that aims to find a correspondence between text in the scanned image and its existing Unicode counterpart, a correspondence which can then be used as training data. The alignment task can be approached with heuristic methods dedicated to certain types of manuscripts, or with weakly trained systems reducing the required amount of annotations. In this article, we propose a novel learning-based alignment method based on fully convolutional object detection that does not require any human annotation at all. Instead, the object detection system is initially trained on synthetic printed pages using a font and then adapted to the real manuscripts by means of self-training. On a dataset of historical Vietnamese handwriting, we demonstrate the feasibility of annotation-free alignment as well as the positive impact of self-training on the character detection accuracy, reaching a detection accuracy of 96.4% with a YOLOv5m model without using any human annotation.

scholarly journals The role of gaze direction and mutual exclusivity in guiding 24-month-olds' word mappings

2010 ◽  
Vol 28 (2) ◽  
pp. 449-465 ◽  
Author(s):  
Susan A. Graham ◽  
Elizabeth S. Nilsen ◽  
Sarah Collins ◽  
Kara Olineck
Keyword(s):  
Gaze Direction ◽  
Mutual Exclusivity
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