Integrating Knowledge and Reasoning in Image Understanding
Abstract
Deep learning based data-driven approaches have been successfully applied in various image understanding applications ranging from object recognition, semantic segmentation to visual question answering. However, the lack of knowledge integration as well as higher-level reasoning capabilities with the methods still pose a hindrance. In this work, we present a brief survey of a few representative reasoning mechanisms, knowledge integration methods and their corresponding image understanding applications developed by various groups of researchers, approaching the problem from a variety of angles. Furthermore, we discuss upon key efforts on integrating external knowledge with neural networks. Taking cues from these efforts, we conclude by discussing potential pathways to improve reasoning capabilities.
Cite
Text
Aditya et al. "Integrating Knowledge and Reasoning in Image Understanding." International Joint Conference on Artificial Intelligence, 2019. doi:10.24963/IJCAI.2019/873Markdown
[Aditya et al. "Integrating Knowledge and Reasoning in Image Understanding." International Joint Conference on Artificial Intelligence, 2019.](https://mlanthology.org/ijcai/2019/aditya2019ijcai-integrating/) doi:10.24963/IJCAI.2019/873BibTeX
@inproceedings{aditya2019ijcai-integrating,
title = {{Integrating Knowledge and Reasoning in Image Understanding}},
author = {Aditya, Somak and Yang, Yezhou and Baral, Chitta},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2019},
pages = {6252-6259},
doi = {10.24963/IJCAI.2019/873},
url = {https://mlanthology.org/ijcai/2019/aditya2019ijcai-integrating/}
}