Learning from Multimedia Data with Incomplete Information
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. Doctoral Consortium. Pages 4921-4922.
https://doi.org/10.24963/ijcai.2021/692
Keywords
- Computer Vision: Recognition: Detection, Categorization, Indexing, Matching, Retrieval, Semantic Interpretation
- Machine Learning: Deep Learning
- Data Mining: Feature Extraction, Selection and Dimensionality Reduction
- Computer Vision: Language and Vision
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.
@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 = {Prof. 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},
}
TY - CPAPER TI - Learning from Multimedia Data with Incomplete Information AU - Tao, Renshuai T2 - Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21 PB - International Joint Conferences on Artificial Intelligence Organization PY - 2021 SP - 4921 EP - 4922 DO - 10.24963/ijcai.2021/692 UR - https://doi.org/10.24963/ijcai.2021/692 ER -Copied
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