Research on the Brain-inspired Cross-modal Neural Cognitive Computing Framework
This work addresses modeling challenges in brain-inspired computing for multimedia and multimodal information processing, but appears incremental as it builds on existing models.
The paper tackled the problem of modeling brain-inspired intelligence by proposing a semantic-oriented hierarchical Cross-modal Neural Cognitive Computing (CNCC) framework, which is claimed to effectively improve semantic processing performance for multimedia and cross-modal information.
To address modeling problems of brain-inspired intelligence, this thesis is focused on researching in the semantic-oriented framework design for multimedia and multimodal information. The Multimedia Neural Cognitive Computing (MNCC) model was designed based on the nervous mechanism and cognitive architecture. Furthermore, the semantic-oriented hierarchical Cross-modal Neural Cognitive Computing (CNCC) framework was proposed based on MNCC model, and formal description and analysis for CNCC framework was given. It would effectively improve the performance of semantic processing for multimedia and cross-modal information, and has far-reaching significance for exploration and realization brain-inspired computing.