VLM can potentially democratize various business, scientific, medical, artistic, and consumer use cases. Automating and simplifying tasks can empower a wide range of users. Typical applications include answering questions, writing captions about images, and generating new pictures based on prompts. Let’s take a deeper look at the model.
What is Visual Language Modeling?
The Visual Language Model is a fusion of visual and natural language models. It inputs images and their respective textual descriptions and learns to associate knowledge from both models. The visual part of the model captures spatial features from images, while the linguistic model encodes information from text.
According to the above, we understand this means combining various visual machine learning (ML) algorithms with a large transformer-based language model (LLM). Current VLMs include OpenAI’s GPT-4, Google Gemini, and the open-source LLaVA.
VLM Working Principle
Image and text encoders are at the heart of a visual language model. The image encoder is responsible for converting images into vector representations the model can understand, while the text encoder converts natural language text into vector representations. These two encoders learn together during the model training process, and enabling the model to understand the relationship between images and text, thus enabling cross-modal information interaction.
Implications for the development of VLM
Traditional ML and AI models focus on one task. They can often excel at simple visual tasks such as recognizing characters in a printed document, identifying defective products, or recognizing faces with proper training. However, they often struggle with larger contexts.
VLM helps bridge the gap between visual representations and humans’ accustomed ways of thinking about the world, and This is where the concept of scale space comes into play.
Humans are experts at spanning different levels of abstraction. For example, we see a small pattern and can quickly understand how it connects to the larger context of which this image forms a part.
LVM represents an essential aspect of automating this process, for example, when we see a car with a dent parked in the middle of the road with an ambulance nearby, we immediately know there may have been a crash, even if we didn’t see it. After that, We may imagine stories about how it happened, look for evidence to support our hypothesis and think about how we might avoid a similar fate.
VLM Applications
Some of the many applications of VLM include:
Adding captions to images: Automatically generated concise captions help users quickly understand images and improve indexing and searching in large libraries.
Visual Q&A: Visual language models can answer natural language questions given an image, providing users with intelligent Q&A services.
Visual Abstracting: Write short summaries of visual information about organization charts, medical images, or equipment maintenance processes.
Image Text Search: Allows users to find images of things related to their query, such as finding a product using a different set of words than its official product description.
Image Generation: This function generates a compliant image based on a textual description provided by the user, providing new ideas for creative design.
Conclusion
Visual language modeling, as a cross-modal information interaction technology, opens new opportunities for the development of the artificial intelligence field.
By better understanding the working principle, practical application, and future development trends of visual language models, we can better utilize this technology to solve practical problems. But everything could be better. VLM may help frame critical business questions, but experts need to be consulted before making any significant decisions.
Moreover, since VLM is the result of capturing many features and generating complex relationships, it will take a lot of work for the creator to adjust once a mistake is made.
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