Dissertation

Exploring Deep Learning for Intelligent Image Retrieval

This thesis mainly focuses on cross-modal retrieval and single-modal image retrieval via deep learning methods, i.e. by using deep convolutional neural networks.

Author

Chen, W.

Date

13 October 2021

Links

Thesis in Leiden Repository

This thesis mainly focuses on cross-modal retrieval and single-modal image retrieval via deep learning methods, i.e. by using deep convolutional neural networks.
For cross-modal retrieval, Shannon information entropy and adversarial learning are integrated to learn a common latent space for image data and text data. Furthermore, this thesis explores single-modal image retrieval in an incremental learning context to reduce the catastrophic forgetting of deep models, thereby expanding the continuous retrieval ability. The efficacy of the proposed methods in this thesis is verified by thorough experiments on the considered datasets. This thesis also gives an overview of new ideas and trends for multimodal content understanding.

• convolutional neural network
• deep learning