Towards Video Captioning with Naming: A Novel Dataset and a Multi-modal Approach

2017 ◽  
pp. 384-395 ◽  
Author(s):  
Stefano Pini ◽  
Marcella Cornia ◽  
Lorenzo Baraldi ◽  
Rita Cucchiara
Keyword(s):  
Modal Approach ◽  
Video Captioning

scholarly journals Piezoresistive dynamic simulations of FDM 3D-Printed embedded strain sensors: a new modal approach

2019 ◽  
Vol 24 ◽  
pp. 390-397 ◽  
Author(s):  
Marco Maurizi ◽  
Filippo Cianetti ◽  
Janko Slavič ◽  
Guido Zucca ◽  
Massimiliano Palmieri
Keyword(s):  
Strain Sensors ◽  
Dynamic Simulations ◽  
Modal Approach ◽  
3D Printed

Understanding temporal structure for video captioning

2019 ◽  
Vol 23 (1) ◽  
pp. 147-159
Author(s):  
Shagan Sah ◽  
Thang Nguyen ◽  
Ray Ptucha
Keyword(s):  
Temporal Structure ◽  
Video Captioning

scholarly journals Correction to: Attention based video captioning framework for Hindi

2021 ◽  
Author(s):  
Alok Singh ◽  
Thoudam Doren Singh ◽  
Sivaji Bandyopadhyay
Keyword(s):  
Video Captioning

Syntax-guided Hierarchical Attention Network for Video Captioning

2021 ◽  
pp. 1-1
Author(s):  
Jincan Deng ◽  
Liang Li ◽  
Beichen Zhang ◽  
Shuhui Wang ◽  
Zhengjun Zha ◽  
...  
Keyword(s):  
Attention Network ◽  
Video Captioning

scholarly journals Olympic Games Event Recognition via Transfer Learning with Photobombing Guided Data Augmentation

2021 ◽  
Vol 7 (2) ◽  
pp. 12
Author(s):  
Yousef I. Mohamad ◽  
Samah S. Baraheem ◽  
Tam V. Nguyen
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Data Augmentation ◽  
Olympic Games ◽  
Event Recognition ◽  
Surveillance Systems ◽  
Video Captioning ◽  
Practical Applications ◽  
Sport Events ◽  
The Olympic Games

Automatic event recognition in sports photos is both an interesting and valuable research topic in the field of computer vision and deep learning. With the rapid increase and the explosive spread of data, which is being captured momentarily, the need for fast and precise access to the right information has become a challenging task with considerable importance for multiple practical applications, i.e., sports image and video search, sport data analysis, healthcare monitoring applications, monitoring and surveillance systems for indoor and outdoor activities, and video captioning. In this paper, we evaluate different deep learning models in recognizing and interpreting the sport events in the Olympic Games. To this end, we collect a dataset dubbed Olympic Games Event Image Dataset (OGED) including 10 different sport events scheduled for the Olympic Games Tokyo 2020. Then, the transfer learning is applied on three popular deep convolutional neural network architectures, namely, AlexNet, VGG-16 and ResNet-50 along with various data augmentation methods. Extensive experiments show that ResNet-50 with the proposed photobombing guided data augmentation achieves 90% in terms of accuracy.

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