Modeling High-Order Relationships: Brain-Inspired Hypergraph-Induced Multimodal-Multitask Framework for Semantic...

Area of Science:

• Artificial Intelligence
• Cognitive Science
• Computational Linguistics

Background:

• Semantic comprehension involves understanding human intentions (sentiment, humor, sarcasm) from multimodal data.
• Existing methods struggle with high-order relationships in multimodal-multitask learning.
• Human cognition offers a model for processing multimodal and multitask information.

Purpose of the Study:

• To bridge the gap between multimodal and multitask learning for semantic comprehension.
• To propose a brain-inspired framework for understanding complex human intentions.
• To address challenges in modeling intramodal, intermodal, and intertask relationships.

Main Methods:

• Developed a hypergraph-induced multimodal-multitask (HIMM) network.
• Utilized monomodal, multimodal, and multitask hypergraph networks to model different relationship orders.
• Incorporated temporal and spatial hypergraph constructions for sequential and spatial data.
• Implemented a hypergraph alternative updating algorithm for vertex and hyperedge convergence.

Main Results:

• The HIMM network effectively models high-order intramodal, intermodal, and intertask relationships.
• Temporal and spatial hypergraph constructions enhance relationship modeling in structured data.
• Experimental results on a two-modality, five-task dataset demonstrate HIMM's effectiveness.
• The proposed approach successfully improves semantic comprehension capabilities.

Conclusions:

• The hypergraph-induced multimodal-multitask network provides a robust framework for semantic comprehension.
• HIMM's brain-inspired design effectively tackles complex multimodal and multitask learning challenges.
• This work advances the field by integrating hypergraph theory for modeling intricate semantic relationships.