IJCAI 2021poster0 citations
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.
Computer Vision: Recognition: Detection, Categorization, Indexing, Matching, Retrieval, Semantic InterpretationMachine Learning: Deep LearningData Mining: Feature Extraction, Selection and Dimensionality ReductionComputer Vision: Language and Vision
BibTeX
@inproceedings{ijcai2021p692,
title = {Learning from Multimedia Data with Incomplete Information},
author = {Tao, Renshuai},
booktitle = {Proceedings of the Thirtieth International Joint Conference on
Artificial Intelligence, {IJCAI-21}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Zhi-Hua Zhou},
pages = {4921--4922},
year = {2021},
month = {8},
note = {Doctoral Consortium},
doi = {10.24963/ijcai.2021/692},
url = {https://doi.org/10.24963/ijcai.2021/692},
}