@inproceedings{647fcf40204b450f934582e4b48cad58,
title = "IOD-CNN: Integrating object detection networks for event recognition",
abstract = "Many previous methods have showed the importance of considering semantically relevant objects for performing event recognition, yet none of the methods have exploited the power of deep convolutional neural networks to directly integrate relevant object information into a unified network. We present a novel unified deep CNN architecture which integrates architecturally different, yet semantically-related object detection networks to enhance the performance of the event recognition task. Our architecture allows the sharing of the convolutional layers and a fully connected layer which effectively integrates event recognition, rigid object detection and non-rigid object detection.",
keywords = "CNN architecture, Event recognition, Malicious events, Multitask learning, Object detection",
author = "Sungmin Eum and Hyungtae Lee and Heesung Kwon and David Doermann",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 24th IEEE International Conference on Image Processing, ICIP 2017 ; Conference date: 17-09-2017 Through 20-09-2017",
year = "2017",
month = jul,
day = "2",
doi = "10.1109/ICIP.2017.8296406",
language = "English",
series = "Proceedings - International Conference on Image Processing, ICIP",
publisher = "IEEE Computer Society",
pages = "875--879",
booktitle = "2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings",
address = "United States",
}