Experts Collaboration Learning for Continual Multi-Modal Reasoning.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Computer Vision

Background:

• Multi-modal reasoning integrates visual and textual data for complex AI tasks.
• Current methods rely on offline learning, limiting adaptability to new reasoning types.
• Continual multi-modal reasoning addresses lifelong learning but faces challenges like catastrophic forgetting.

Purpose of the Study:

• To develop a novel approach for continual multi-modal reasoning.
• To enable AI models to learn new reasoning tasks continuously without forgetting.
• To address the limitations of offline learning in dynamic AI environments.

Main Methods:

• Proposed a brain-inspired expert collaboration network (Expo).
• Incorporated multiple, dynamically assembled, task-specific learning blocks (experts).
• Designed an effective strategy for automatic selection and updating of experts.

Main Results:

• The Expo network demonstrated effective learning of new multi-modal reasoning tasks.
• The model successfully consolidated previously learned reasoning skills, mitigating forgetting.
• Extensive experiments validated the efficacy of the proposed approach.

Conclusions:

• The expert collaboration network offers a promising solution for continual multi-modal reasoning.
• This approach enhances AI's ability for lifelong learning in complex reasoning scenarios.
• The dynamic expert assembly and selection strategy is key to overcoming forgetting in continual learning.