In multimodal AI, how can different types of data (such as images, text, and speech) be fused to jointly train models?

Okay, no problem. Let's talk about this in plain language.

Giving AI Eyes and Ears: A Chat About Data Fusion in Multimodal AI

Imagine how we ourselves understand the world. You see a cat (image), hear it meow (audio), and simultaneously, the word 'cat' (text) comes to mind. Our brains effortlessly integrate this information to form a complete understanding: "This is a cat meowing."

What multimodal AI aims to do is mimic this process. But there's a challenge: an image is a collection of pixels, audio is a sound wave, and text is a string of characters. Their "formats" are completely different. It's like trying to "add" an Excel spreadsheet, an MP3 file, and a JPG image together; direct addition simply won't work.

So, AI scientists have devised several clever "fusion" methods to translate these different data formats into a "common language" that AI can understand. There are primarily a few schools of thought:

1. Early Fusion - "The Big Stew"

This is the most direct and simplest, albeit crude, method.

• Like making fruit and vegetable juice: Whether it's apples, bananas, or spinach, you wash them clean and throw them all into a juicer to make a mixed juice.
• How AI does it: At the very beginning of model training, image, text, and audio data are processed into their most basic digital signals (e.g., flattening an image into a long string of numbers, converting text into a long string of numbers). Then, they are crudely "concatenated" together to form a massive data "vector," which is then fed to a single model for learning.
• Pros: Simple and straightforward.
• Cons: It's too crude. The unique "personalities" of different data types can easily be lost early on. For instance, an image's spatial structure information or audio's temporal information might be lost during concatenation, often leading to suboptimal results.

2. Late Fusion - "Act Separately, Meet at the End"

This method is more "strategic."

• Like an expert team: An image expert analyzes pictures, an audio expert analyzes sounds, and a text expert analyzes words. After each reaches their professional conclusions, they convene to synthesize everyone's opinions and make a final judgment.
• How AI does it: For images, text, and audio, three independent "expert models" are trained separately. For example, an image model is used to determine what's in a picture, and an audio model to identify what a sound is. Once these three models have provided their "high-level judgments" (e.g., the image model says, "I see a furry animal," and the audio model says, "I hear meowing"), these judgment results are then fused, allowing a final "decision model" to make the ultimate call.
• Pros: Each "expert model" can fully leverage its strengths, preserving the unique characteristics of each data type.
• Cons: The "experts" don't communicate during their work. They miss opportunities to inspire and corroborate each other during the analysis. For example, if the audio model hears "meow" and could tell the image model earlier, the image model might locate the cat in the picture more quickly.

3. Hybrid/Intermediate Fusion - "Work and Communicate, Collaborate"

This is currently the most mainstream and effective method. It combines the advantages of the two methods above.

• Like an efficient special operations team: Although the team members (modules processing different data) have their respective roles, throughout the entire mission, they continuously communicate information, adjust strategies, and cover each other via walkie-talkies (fusion mechanisms).
• How AI does it: Within the model, separate processing "channels" are established for images, text, and audio, but these channels are not entirely independent. At several "checkpoints" during processing, they exchange information. One of the most famous techniques here is the "Attention Mechanism," especially "Cross-Attention."
  • Simply put, 'Cross-Attention': When the model processes a sentence like "A black cat is rolling on the grass," the text processing module, upon seeing the word "black cat," will use this "attention mechanism" to tell the image processing module: "Hey! Pay more attention to the black, cat-like area in the picture!" Conversely, when the image module analyzes the cat's area, it will tell the text module: "What I'm seeing has whiskers and a tail, which perfectly matches the characteristics of a 'cat.'"
  • Through this method, different types of data continuously "converse" and "guide" each other within the model, achieving deep, dynamic fusion. Many powerful current models (such as DALL-E, which can draw based on text, and CLIP, which can understand image content) use these types of techniques.

In Summary

You can think of it this way:

• Early fusion is "you in me, me in you," but it easily turns into a messy stew.
• Late fusion is "you are you, I am I," and then we collaborate at the end.
• Hybrid fusion is "we maintain independence yet constantly collaborate closely," which is the most efficient team collaboration model.

The ultimate goal for all of them is to enable AI models to build a richer, more multi-dimensional cognitive model that is closer to human understanding, thereby allowing them to better comprehend this complex world.