Fusion Strategies: Combining Multiple Modalities

What problem does it solve?

This Skill addresses the challenge of integrating information from various data types (text, image, audio) into a single, coherent representation for AI models.

Core Features & Use Cases

• Early Fusion: Combines raw inputs before encoding for efficiency and tight synchronization.
• Late Fusion: Encodes modalities separately then combines, offering modularity and handling missing data.
• Hybrid Fusion: Blends early and late fusion for a balance of performance and flexibility.
• Cross-Attention: Explicitly models modality interactions for state-of-the-art performance.
• Use Case: Building a video analysis system that needs to understand spoken words (audio), visual content (image), and accompanying text descriptions, choosing the best fusion method for accuracy and speed.

Quick Start

Implement a hybrid fusion model combining image and audio early, then fusing with text late.