Learning from Multimedia Data with Incomplete Information
Abstract
Traditional deep learning methods are based on the condition that the data is of high-quality, which means the data information is highly available. However, data in these scenes often have the characteristics of large background noise, lack of sample content, small target, serious occlusion and a small number of samples. The application of related tasks in real open scenarios is very important, so it is urgent to make full use of these incomplete information data accurately.
Cite
Text
Tao. "Learning from Multimedia Data with Incomplete Information." International Joint Conference on Artificial Intelligence, 2021. doi:10.24963/IJCAI.2021/692Markdown
[Tao. "Learning from Multimedia Data with Incomplete Information." International Joint Conference on Artificial Intelligence, 2021.](https://mlanthology.org/ijcai/2021/tao2021ijcai-learning/) doi:10.24963/IJCAI.2021/692BibTeX
@inproceedings{tao2021ijcai-learning,
title = {{Learning from Multimedia Data with Incomplete Information}},
author = {Tao, Renshuai},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2021},
pages = {4921-4922},
doi = {10.24963/IJCAI.2021/692},
url = {https://mlanthology.org/ijcai/2021/tao2021ijcai-learning/}
}