James Summers (1828-1891) is the first British professor who conducted systematic research on Chinese grammar. As a former missionary, he had been directly exposed to vernacular Chinese, which... Show moreJames Summers (1828-1891) is the first British professor who conducted systematic research on Chinese grammar. As a former missionary, he had been directly exposed to vernacular Chinese, which enabled him to teach and research it at King’s College London in his later career. This dissertation provides a complete picture of his research on Chinese grammar throughout his publications. It further brings Summers to prominence in the historiography of linguistics. By tracing which and whose ideas inspired him and who he, in turn, influenced, this study identifies his position relative to other linguists. The dissertation claims that Summers was able to integrate the research of his predecessors and arrange their findings and conclusions in his own clearly pedagogically oriented research, abandoning the purely theoretical conclusions to help his students learn Chinese efficiently. Show less
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... Show moreThis 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. Show less
