Skip to content

Latest commit

 

History

History
119 lines (72 loc) · 6 KB

README.md

File metadata and controls

119 lines (72 loc) · 6 KB

qbind

image

Problem

  1. There are many modalities, much more than the number of senses human have.
  2. Relationship between objects have different meaning: A chimp and a human are similar animals on the text level but both generate quite different audio

Solution

einops

from einops import rearrange, reduce, repeat
# rearrange elements according to the pattern
output_tensor = rearrange(input_tensor, 't b c -> b c t')
# combine rearrangement and reduction
output_tensor = reduce(input_tensor, 'b c (h h2) (w w2) -> b h w c', 'mean', h2=2, w2=2)
# copy along a new axis
output_tensor = repeat(input_tensor, 'h w -> h w c', c=3)

qbind

Introducing qbind: A Language for Cross-Modality Data Binding

In today's world, data comes in various forms and modalities. From text and images to audio and video, the amount of information that can be represented and processed has grown exponentially. However, dealing with multi-modal data sets can be challenging, especially when it comes to understanding the relationships and similarities between different modalities. To address this issue, we propose a new language called qbind, which aims to make it easier for humans to work with multi-modal data.

What is qbind?

Qbind is a new language designed to allow humans to intuitively process and embed data across different modalities. By using qbind, users can define relationships between various modalities by specifying simple operations and transformations on the data.

How does qbind work?

To demonstrate the power of qbind, let's take a look at some examples:

  1. Let's say you have textual data and image data, and you want to find a way to represent both text and images in 1536-dimensional space. In qbind, you can write the following expression:
t x i -> 1536

This expression tells qbind to combine the text ("t") and image ("i") data in a way that the resulting representation has 1536 dimensions.

  1. Now, let's take this a step further and consider a scenario where you have text, image, and audio data. You want to find a way to represent all three modalities in 2048-dimensional space. In qbind, you can write the following expression:
i x a x t -> 2048

This expression informs qbind to combine the image ("i"), audio ("a"), and text ("t") data in a manner that produces a 2048-dimensional representation.

Benefits of qbind

Qbind has several advantages when it comes to handling multi-modal data:

  1. Intuitive: The qbind language is designed to be simple and easy to understand, making it accessible to users with different levels of expertise.

  2. Flexible: Qbind allows users to define their transformations and relationships between different modalities, making it highly adaptable to various use cases.

  3. Scalable: Qbind is designed for scalability, making it suitable for working with large multi-modal data sets.

Conclusion

Qbind is a powerful new language that aims to simplify the process of working with multi-modal data sets. By providing an intuitive and flexible way to define relationships between different modalities, qbind can help users better understand and process the vast amount of information available in today's world. With qbind, researchers and developers can more easily work with multi-modal data, opening up new possibilities for understanding the intricate relationships between text, images, audio, and more.

Other examples

Of course, let's dive deeper into qbind language with more examples, including depth modality and heatmaps, as well as slicing and division operations.

Example 1: Depth Modality and Heatmap

Suppose you have depth modality ("d") and heatmap modality ("h") data, and you want to represent the combination of these two modalities in a 1024-dimensional space. In qbind, you can write the following expression:

d x h -> 1024

This expression informs qbind to combine the depth ("d") and heatmap ("h") data in a way that produces a 1024-dimensional representation.

Example 2: Slicing and Division

Suppose you have textual data ("t"), image data ("i"), and audio data ("a"), and you're interested in dividing each modality's data into two equal parts. You'd like to combine the first half of the text, image, and audio data into a single representation of 256 dimensions, and do the same with the second halves. In qbind, you can write the following expressions:

slice t -> t1 t2
slice i -> i1 i2
slice a -> a1 a2

(t1 x i1 x a1) -> 256
(t2 x i2 x a2) -> 256

In these expressions, the "slice" operation divides each modality's data into two equal parts, denoted as t1, t2, i1, i2, a1, and a2. Then, qbind combines the first half of each modality into a 256-dimensional representation and does the same for the second half.

Example 3: Combining Sliced Modalities and Different Representations

Suppose you want to combine text, image, audio, depth, and heatmap data, but you also want to keep the information from text and image data separate from audio, depth, and heatmap data. You can represent text and image in a 512-dimensional space and audio, depth, and heatmap in a 768-dimensional space. In qbind, you might write the following expressions:

slice t -> t1 t2
slice i -> i1 i2

(t1 x i1) -> 512
(a x d x h) -> 768

Here, qbind first slices text and image modalities into two equal parts. Then, it combines the first half of the text and image modalities into a 512-dimensional representation, and separately, it combines audio, depth, and heatmap modalities into a 768-dimensional representation.

These examples demonstrate the flexibility and expressiveness of the qbind language, enabling users to work with different modalities and perform various slicing and division operations. With qbind, users can intuitively define transformations and relationships between multiple data modalities and represent their combined information efficiently.

Roadmap