Research on the Brain-inspired Cross-modal Neural Cognitive Computing Framework

TL;DR

This paper proposes a brain-inspired hierarchical framework for multimedia and multimodal semantic processing, enhancing cross-modal information understanding by modeling neural cognitive mechanisms.

Contribution

It introduces the CNCC framework based on MNCC, providing a formal description and analysis to improve semantic processing in brain-inspired computing.

Findings

Enhanced semantic processing performance for multimedia data

Effective cross-modal information understanding

Framework grounded in neural and cognitive principles

Abstract

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.