Meta Introduces Vision Language Models: Superior Performance and Advanced Features

The Vision Language Model (VLM) is an extension of the Large Language Model (LLM) with visual capabilities. They can assist by analysing a paper and then creating a text or image based on the part highlighted, making any image based on the text from the image, or even assisting in image-to-text.

Researchers at Meta recently shared a paper called ‘An Introduction to Vision-Language Modeling’ to help people understand how to connect vision and language. The paper explains how these models work, how to train them, and how to evaluate them.

This new approach is more effective than older methods like CNN-based image captioning, RNN and LSTM networks, encoder-decoder models, and object detection techniques. Traditional methods often can’t handle complex spatial relationships, integrate diverse data types, or scale to more sophisticated tasks as well as the new vision-language models can.

How does the Vision Language Model work?

VLMs use advanced algorithms to analyze pictures and words. They can understand the image or content provided to them and can even draw a pattern or find any relationship between them. 

Challenges in VLMs

A picture is made up of thousands and thousands of pixels and each of these pixel boxes consists of a single color which can sometimes be very hard for the models to analyze interpret and draw a pattern or a relation. However, texts and words are letters and numbers which is quite easy for the model to analyze and interpret.  Thus it can easily be concluded that although the entire concept seems to be very fancied, asking the model to draw a relation between an image and a text appears very complex.

Training a VLM

To make a VLM, the first and foremost thing is to find large datasets and then feed it to the system in order to train the model. There are mainly 4 types: 

1. Contrastive training – This mainly involves feeding two different images to a model and asking to find the differences between them
2. Masking – This involves feeding an image to the model however, shadowing a particular object and asking the model to analyze the rest of the image and guess what is being hidden or masked.
3. Using pre-trained parts – This means using parts of other algorithms that have already been trained to help the VLM
4. Generative training – This involves having the computer create new pictures based on a description.

Source: Research Paper

Cleaning Data for VLMs

To train VLMs, researchers need a lot of data. However, not all data is useful. The researchers at Meta have come up with three ways to clean data for VLMs:

1. Heuristic methods – involve using thumb to decide whether the data provided is required or not
2. Bootstrapping – This involves feeding the model with some data and once it is trained then the VLM itself will be used to find more data
3. Making a diverse dataset – This involves making sure the data includes a wide variety of pictures and words.

Generative AI vs Predictive AI: Check Key Differences Between them

Testing VLMs

To make sure VLMs are working well, they need to be tested. There are several ways to test VLMs, including

1. Visual Question Answering (VQA) – VQA involves asking the computer questions about a picture and seeing if it gives the right answer
2. Reasoning tasks – Reasoning tasks involve having the computer solve problems based on a picture and a description.
3. Dense: using manual feedback by humans in order to analyze whether the results are accurate or not.

Source: Research Paper

Conclusion

Thus, VLM shows the world of AI a new way to analyze and interpret images. This will help us to find any object or even create any images or objects from the description that will be provided to it. Although there are certain challenges to the whole method, researchers at Meta are working to make it more precise and efficient. Thus, VLM has the potential to revolutionize the way humans interact with AI.

What are the key differences between large language models (LLMs) and generative AI?