---
title: "Learning from Multimedia Data with Incomplete Information"
authors:
  - "Renshuai Tao"
doi: "10.24963/ijcai.2021/692"
url: "https://ijcai.org/proceedings/2021/692"
pdf: "https://ijcai.org/proceedings/2021/0692.pdf"
venue: "Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence"
year: 2021
pages: "4921-4922"
track: "Doctoral Consortium"
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"
---

# Learning from Multimedia Data with Incomplete Information

Renshuai Tao

*Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence*, pages 4921-4922. DOI: [10.24963/ijcai.2021/692](https://doi.org/10.24963/ijcai.2021/692)

## 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.