Learning from Multimedia Data with Incomplete Information

Learning from Multimedia Data with Incomplete Information

Renshuai Tao

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence
Doctoral Consortium. Pages 4921-4922. https://doi.org/10.24963/ijcai.2021/692

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.
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